How to Land a $700K+ AI PM Job Using AI by Google PM Alex Rechevskiy

In this video, we'll show you exactly how to find a PM job with AI. I earned over $900,000 when I was a PM at Google. Alex Rachevsky has helped hundreds of PM's land jobs. Some of them $600,000, $700,000 AI PM jobs. And today he's going to break down all the steps you need to land a job with AI. The AI is not magic. It can accelerate things and can make you feel like a superhero. But if it won't help if you don't understand how this stuff actually works, sometimes you just, you're just wasting time. If you're just applying with a generic resume, if it does not have these elements, it will be skipped. So you can apply to 100 and it will be completely just waste the entire 100. For anyone who wants the deep dive, well, that's what this is. These three lines right here, that's the whole game. Just put everything you need right in there and assume that every recruiter is only going to read this thing. This is insane. And what I've noticed is that these AI PM jobs are paying way better than regular PM jobs. 30 to 40% more. No, we're going to get to the next very important element, which is the outreach and people that just do the applications. I'm sure you've heard of people telling you I can't get a call back. So we've been talking about $700,000 plus jobs. But a lot of PMs, they're just had $140,000. Is it actually realistic for people to land these high-paying jobs? Really quickly, I think a crazy stat is that more than 50% of you listening are not subscribed. If you can subscribe on YouTube, follow on Apple or Spotify podcasts. My commitment to you is that we'll continue to make this content better and better. And now on to today's episode. Alex Rachevsky has helped hundreds of PMs land jobs. Some of them $600,000 AI PM jobs. And today he's going to break down all the steps you need to land a job with AI. Alex, welcome to the podcast. That's good to be here, Rachevsky. I think this is our fourth time collaborating. And the big change that has happened is the rise of the AI product management role. I just pulled some data from LinkedIn where every year I go and search how many open product management jobs there are. And what I learned is that the product management job landscape in 2023, 2% of the roles mentioned AI in 2025, 20% of the roles mentioned AI. What do you think of this trend? Yeah, that sounds about right. Pretty much you can't get a PM job these days without some mention of AI. So that's for sure true. And what I've noticed is that these AI PM jobs are paying way better than regular PM jobs. 30 to 40% more. Group product managers in the 25th to 75th percentile of AI PM jobs are making 360 to $600,000. CPOs are making well over $2 million. Is this what you're seeing in the market? We're definitely seeing AI PM jobs have wider bands of compensation. These days, especially so. And it seems to be a trend that's accelerating. This is the level's FIA data for Google PMs. You were a PM at Google. Are these bands accurate for Google AI PMs? Now, let's see. A group PM, 726,000, 286 base, stock 326, that's about right. Now, of course, these are either mediums or averages. So what I usually tell people is that there's a lot of outliers there. So yes, these numbers do look accurate. So that's the facts, folks. There's tons of AI PM jobs. They pay extremely well. So let's not get keep the knowledge, Alex. How do you find an AI PM job with AI? All right. So there's a few key use cases. I want you to understand that we are going to be leveraging AI wherever it's actually useful in the stage to accelerate ourselves. And I'm going to walk you through a few cases. We're going to be creating resume. We're going to be refining it. We're going to be creating company list. We're going to be doing outreach, direct outreach and helping with referrals. And finally, some interview prep. Some guided interview prep with AI. But first, I want to walk you through a little bit of the philosophy behind all of this. So you can understand how recruiters things, hiring manager think and how this whole process works. So you can optimize your performance a little bit better. So first of all, just a reminder, quick reminder that AI is not magic. It can accelerate things and it can make you feel like a superhero. But if it won't help if you don't understand how this stuff actually works. So fundamentally, I want to run you through some of this stuff. Don't focus throughout your job search on what you want. Instead, focus on what the company actually wants. And they've laid it out in a few places which we're going to go over. Try to turn this whole concept of what do I want, what's next for me into, what did they need, what problems can I actually solve. And remember, this is super important on the path to your next job. First, you're going to get the call back. Then you're going to get the interviews. Then you're going to get the offer. It's not apply, get the offer. So your resume at the beginning of these stages is just to get the call back, not the offer. Now, where are we going to actually start our journey here? It's going to start with the job description. Of course, we're going to create a resume. But a job description is where you're going to find the information. It's going to tell you what problems hiring manager and the company actually has. And recruiters, when they think about going through your resume and your profile, they're going to be skimming for these three to five key skills or experiences that match whatever was given to them in the job description or by the hiring manager. So this is just the most important fundamental stuff for you to understand. A job description is not created from the ether. It is created when a hiring manager gets some headcount approved because they have structured the way that they want to approach their job and product development in the next quarter or year. And they've managed to get the actual headcount to pursue that work. So when they do that, they pull up their favorite LLM, Clawed GPT, whatever, and they write their job description. And then they tweak it a little bit and they say, "Yeah, I think I need this, this, and this." So what you're getting is just the best idea of what the hiring manager thinks they need to actually execute on this product plan that they presented to leadership. And the recruiters almost getting like a broken telephone of that. So there's a lot of stuff for us to get through, but I want you to understand like the reality of how this works. So, and another fundamental area that's very important is that because there's a lot of applicants and now people are going to be using AI to apply, so it's going to go even faster, even more applicants for a lot of roles, certain elements are going to be more and more important. So we've highlighted Impact, which is the stuff that you've done, how impactful it was, usually dollars, user, growth, revenue, all of these areas where you've managed to generate impact internally or externally, scope, meaning the breadth, like how big was this product, was it a product feature, or was it a whole product, or a product line, or maybe an entire company, and then recognize ability, which is a huge one, and I know that that leaves out a lot of folks that don't have the necessary recognizability. Nonetheless, I'm here to tell you how it actually is versus how we wish it would be. So, recognizability of the companies on your actual resume are very, very important, and if you don't have any recognizable companies, then step one is pick up some recognizable companies. So these are the three things that are going to matter the most to recruiters. And our job is to make it super easy for the recruiter to look at your resume and say, yes, yes, this person actually meets our requirements. Okay, do we just go over the same thing? We can probably skip this one. All right, so here's how the screening process works. Push yourself in the shoes of recruiter for a moment. They are going to be getting a ton of resumes. They're going to be sitting down at their computer and saying, okay, here we go. Brace yourself. We've got to sit down and review some resumes. They're going to be reviewing 20 resumes that are at the top of their batch. This could be either in a, in a natural software that they use, or it could be in their email, or it could be in some other place where candidates are stored. And they're going to batch screen 10, 20, 30, however many they can get through. They're going to spend just five to seven seconds doing this, unless it's an internal referral, which you can talk about later. And they're going to be looking for those three to five skills that match those magical signals that the hiring manager explained to them. If they don't find them, usually they're rejected. If they are looking for referrals, so let me walk you through this. So they're looking for recognizable brands and experiences. Internal resumes will get a deeper review, but not more than 20 seconds. You're still looking for the same three to five skills. The same recognizable brands, the same experiences. And if you don't hit something from that, then your resume is rejected. Therefore, it's a very simple game to understand. Like if you don't have what is necessary, your resume will be rejected. So a lot of times people come to me and they say, "Alex, I'm sending out so many applications and I don't hear back." And I say, "How many have you sent out?" And they say, "20." I say, "Well, if you expect to hear back from 10%, which would be phenomenal, then from 20, you're going to get one or two responses." So you've got to pace yourself. You've got to understand what you're actually getting into. Numbers are really important, actually, in the job search. It's so important. And if you have one tool, one AI tool that actually we've been playing around with is massive, and they do the whole job application for you, and they try to connect you, and try to simplify some of that stuff. In fact, we're working internally at PCA on our own tool that's going to do the same exact thing, but we're still a couple months away. But what massive does is it tells you right away when you join. They say, "How many interviews would you like to have?" And you say, "One, or two, or five, or ten." They say, "Okay, that's cool." Did you know that if you want to get an interview, you need to get like three or four callbacks. Okay, that's great. And did you know that to get a callback, you need to submit from 30 to 50 to 100 applications? Okay, now you know. So the 1%, 1% is the callback rate expected at massive. I hope you've been enjoying this guide to getting an AIPM job. You see that little picture behind me? That's linear. Linear is the sponsor of today's episode, and it is the task management tool that your engineers will actually love, and it's enabling the future of product management. Their head of product, Nanyu, was on this podcast just a few months ago where he demoed how you can create a task, and then call a coding agent, like code gen, cursor, or cloud code, to code that task, and then you can send it to an on-call engineer, they can do a quick review, and boom, the thing is shipped. PMs can get 10x closer to pixels with tools like linear, so check it out with linear.app/aukash. That's l-i-n-e-a-r.app/a-k-a-s-h. And now back to the episode on how to land an AIPM job with AI job search tools. So when we work in PCA and some of our clients get 10% callback rates, I have to remind them how awesome that is, sometimes we get 12% and 15%, but ordinarily, it's actually 1%, and by the way, the caveat is that, of course, recognizable companies and all these things that were just discussed, those are the way that you get a high callback rate, and by not stepping on your own foot as you go. So if you are fitting the requirements, you've got the impact, there's no red flags, then they may contact you immediately, or they may put you in a list to actually contact you later. The thing is, if you hit a few of the signals that they want, they may look deeper, and that's where your outcomes matters, your impact matters, the fact that you actually drove those initiatives and projects matters as opposed to just you were there. And then you need to make sure there's no red flags. And actually, what we're going to be doing with our AI refinements is making sure that we don't have red flags. We need to make sure that we are not just stuffing skill words in there. And actually, that's something that, as you can imagine, a lot of times, that's what AI does is it will stuff a lot of skills. We're going to try to avoid and get rid of that in our prompts. You want to make sure that there's no missing time blocks. You want to highlight the important areas. You don't want to bring up the things that don't matter at the top and waste the real estate, the screening real estate of your recruiter. So, North Star, what do we want from our resume? It's got to be short. It's got to be readable. Repeat after me. Short and readable, please. And you don't make long resumes. I know you and I have been saying this for a long time. Introduce plenty of hooks at the top. Even when you have a one page resume that's great. I want you to just focus on the top half. And within the top half, focus on the top half. So you're actually doing like a top quarter page is where the meat actually is. And we got to land those hooks just like you were optimize any piece of content nowadays. If you happen to write anything online, you know that the top, the subject line matters the most because that's what controls whether or not you're going to get any people to actually click through and read your resume. And the third most important thing is you need to actually understand where these three to five elements. We're going to work through an example of your together. We're going to make it relevant and we're going to provide proof of impact on outcomes, additional quality signals and eliminate red flags. That's it. So if you just take the seven, you're going to be good to go. Yep. Any questions so far? Let's see it in action. All right, perfect. Let's do it. So here's an example of my favorite resume template. This is the one we use of PCA. It's very, very straightforward. And no, I don't care if he use a variation of this, one that looks similar to that. Just get the spirit of the thing. Now this, I want you to, don't worry if we cannot produce, if we cannot have our AI produce the exact document we need. I don't want you to worry about that too much. Just worry about the content and you can handle the formatting yourself. Yes, you can sit down and spend five minutes and format it so that it's clean and a human reading it is not going to pass out because of your walls of text and you're in your graphics and your tables and your pictures and your all the stuff. Sorry if I'm frustrated because I talk about this all the time and we still every single day just get wacky resumes. Please lean on common sense with the resume. What did I say? The top half is where the most impact is going to be. I'm literally saying on these three lines, you're going to summarize your whole resume. You know how when you're writing your executive summary or you have to update your your leadership and you're going to send an email, it's going to have a TLDR at the top, which has just two or three lines of the thing that happened, the thing they need to know and then like four paragraphs for everybody else and then another click out to a document for anyone who wants the deep dive. Well, that's what this is. These three lines right here, that's the whole game. Just put everything you need right in there and assume that every recruiter is only going to read this thing. So the template here is experienced X field PM or X plus years in field, then your expertise, like your verticals industry specialities, some famous companies, those recognizable names you were talking about, revenue or customers or client numbers and your education. Yes, and roughly in that order, I again, this is not gospel. By any time you're wondering, hey, what should I do? Don't wonder what would Alex do or what would Akash do. Just ask yourself what makes sense to you if you are trying to optimize for a human being recruiter, human being recruiter. And what they want to know is the meat, what have you done, where have you worked? How can you help? Make sense? Now, the rest of this stuff, yes, we can argue. Okay, there's the Google style of bullets. There's Alex's style of bullets. There's like variations. It doesn't matter. Just tell me the things that you did, try to make it actionable, try to start with an action verb, try to cover a variety of PM skills and attributes. And we're going to talk about how we're going to do that with AI. Finally, we get to it. The work. Let's do it. Are you ready? Can't wait. All right. Let's do it. This is for people who are serious about landing their next PM job and they want to make serious money. So this is where the work comes in. So step one is going to be to gather your inputs. This is the scary thing that you've been putting off because you think, oh my god, I've got to do my resume. I haven't done it since five years ago since I joined this company. I don't know where to start. Well, you're in luck. We're going to work through it right now and we're going to leverage AI. The general philosophy is going to be we're going to open up a doc, your favorite Google doc or whatever you use, notion, it doesn't matter. And you're going to just write out responses to the following questions and you're writing out as if you and I were on a call and I was asking these questions and you were answering them. So if you want to type, you can type. If you don't have 120 typing speed per minute, like I do, for example, then you might want to use whisper, which is a tool that I've been using for the last couple months. I've really been enjoying it. And humans typically talk at like 200 words per minute. So you're going to get some good efficiency. Yeah. You and I definitely talk at 200 words per minute. But I know that sometimes when I end up talking for whisper, like when I'm trying to think things through, I usually do about 120, 150, but yes. What I like about whisper is that it captures the spirit of what you're saying a lot better than something like a pure like Mac dictation or something like that. Oh, it's a world. So anyways. Yeah. Yeah. So let's go through these questions. Remember, use this video. Just pause it and then write the answer or speak the answer in the doc together with us. I'm going to blast through this really fast. Just give you a little bit of context. But I want you to pause and work through it. Pause and work through it. Here we go. For every job that you've held starting with the most recent is the one that you remember. Just answer the question. What aspect of the work did you enjoy? Now you may be tempted to go into a 100 different directions. I really like this thing, but not so much that thing and that one time the boss did this and my teammates did that, that's fine. This is the beautiful thing about this is you're just speaking this out. You are going to take all of these inputs and give it to the AI in order to produce our beautiful resume. So feel free to go on as if you and I were having a conversation and you had all the time in the world. What projects did you work on? You can imagine how broad this can be. Well, I have, generally I was working on this project. I was trying to build out this dashboard. But why was I doing it? It's because of this and that and you can go on a lot of down a lot of rabbit holes and I want you to do so. Who did you work with? Name the people that you've worked with and explain their roles. What did they do? What was their function? How did they help? What are you most proud of in general from your work there? And don't be shy. Just say exactly what happened. What other accomplishments or achievements do you remember? Go as granular as you want with this. Remember, we're not going to use anything that is generated that doesn't meet our bar of quality. So just use this to create this big document. By the way, this is going to have another benefit for us when we're going to be working on behavioral stories later on when we're preparing for our interviews. So you're going to need to do this work anyway. That's why I called this the work. What concepts did you ideate or develop? What did you actually come up with? What projects did you plan? Product managers, obviously we plan a lot of projects. What products or features did you actually launch? Think about little features, internal external, anything counts. What products or features did you actually land? So this is where you didn't just launch it, but it actually had a difference on something. So it was useful to somebody. And here we go. What obstacles and difficulties you overcome? Again, the smallest ones to the biggest ones. What problems did you ultimately solve? Think customers, think team members, leadership partners, be broad with this. What did you learn? You can imagine how much you can talk about here. Side note, one thing that we work with our members in PCA is developing their own product principles. Most of the time we work with mid-career and senior folks. So they've developed product principles, but they've never actually spoken them out loud. So this thing right here, what did you learn is going to be the beginning for you to start thinking about everything that you learned over your product career. How did you work with others? Think individuals versus teams, leadership, I see. Did you ever save costs? Did you ever optimize resources? This is we're trying to get to the impact. Did you introduce any tools, technologies, new methodologies to your team, to your org, to your company? Be specific because this is the stuff where we're going to be pulling out these meaningful impacts that you had. Any moments where you have to make difficult decisions, this is going to come up for behavioral as well. We're almost finished. What were these difficult decisions? How did you get through them? Use, talk about your principles and talk about what you actually did. Were there any systems that you helped to improve or streamline? Again, this could be internal or external. Did you mentor or train anyone? How did that impact the team with the individual? How did you contribute to the culture? What about that one party that you thought about after office hangout? What happened? Did you do some interviews for the company? Did you help new PMs that are starting with a company? Think about that. How did you actually make sure that your clients or users were happy in general? And did you do any initiatives outside of your scope? You had your projects and then maybe you helped out another team. You completely forgot about it until I just asked you. In general, as a stopgap, is there anything that you found that we didn't yet talk about that someone else hadn't noticed? And to that end, any awards, recognitions, any even meaningful feedback. I always tell PMs to take screenshots of the, hey, thank you. You helped the team out a lot. I really appreciate little emails, little slack messages, take screenshots of those. These would be a great place to put them. All right, that's it. So many questions. All the stuff. It's a lot of work, but these are the raw inputs. So where does this, where do we go from here? Yes, all right. So where do we go? So our job is we're going to tell our AI assistant to take everything, the raw data that we just came up with and group them in such a way as to create a good coverage for the general skills. Basically, when I think about resumes, I think about bundling the skills and attributes in a few areas and you can see the beginning of them here, the first big area is product development. Then we've got leadership and execution with strategy and planning, business and marketing, project management and technical and analytical. Those are the bundles that we've chosen after working with hundreds of PMs. You're welcome to make adjustments of your own, but what we're going to do when we're creating this baseline resume, the philosophy is that we want to take everything you've done and then we want to cover all of these six buckets of base product management attributes in such a way as to give you a comprehensive overview. And of course, we will lead with the impact. We will make sure that we will stack rank the bullets and we will follow the rest of the rules that we laid out. But this is the general categories of content that we want to cover. So let me see if we're going to we're going to do it together. So we got our prompt. This is this one we're going to have you do by yourself and then the next ones that we can run you through an actual case of how it looks like. But the idea is we're telling AI that we're creating a product management resume, which we're going to pull from the raw career history plus rolled titles. This is the titles that you've held. Companies that you worked for and the dates. And we're going to use the following rules, create bullets in the format, action verb context result metric. Again, you can experiment with this, but please don't change action verb and the fact that result and metric are in there. So it has to start with an action verb and result and metric has to be in the bullets some way. You can play around with the context. And we want to cover all of the bullet vault bundles. We call these bullet vault because we will create a resume that is larger than maybe a resume that you'll end up submitting. But it has all of the variations and the different types of bullets that we may need to apply for different jobs. So we call this the bullet vault, which is our kind of baseline resume. So these are the categories again, the six categories plus the seventh communication, collaboration, presentation, storytelling, which should be present throughout. And what we're telling AI here is we want to cover all of these bundles. We want to cover all of these skills and PM attributes. The instruction here, keep most bullets at one line. If you can be two to three lines, no walls of text up to 10 bullets per roll, and we can use the entire 10 bullets and then we can do stack ranking on it to make sure that the most impact fall at the top and we can cut the bottom so we can keep the to our rule of keeping short and relevant resumes. And here's the important one, remove descriptor adjectives and replace with measurable outcomes. And sometimes depending on the output produced, we will also give a few examples of descriptor adjectives. And I'll just give you a few right here. It can be stuff like anything where you're describing something that's subjective, like incredible or robust. Like those are descriptor adjectives. They have no business being in a resume because most things should be quantitative and should be apparent what they do. And these are the instructions that we give. And at the end, you paste your raw career history and please also provide your company names, dates work there, and the specific titles that you help. Yeah, this is the base resume and we will take this resume and we're going to copy and paste it for the rest of our prompts when we work on the rest of our application process. Here we go. So now set that aside, you've got your actual baseline resume. And I remember that's going to be a baseline resume. We're going to use that to refine it to apply to a particular job. And we're going to do it fast with AI with stack ranking and making relevant content. But for now, we're going to take a slight detour because I want to talk about another important thing, which we leverage AI for in PCA. And that is to create your company list, your target company list with AI. So let me just open up the prompt here. What do we want to cover here? We want to cover a few of the buckets that we found to be important and they are the size of the company because we try to target compensation as an important area. So the size of the company is going to be the most representative or the most correlated with the compensation that you can actually unlock. And the size of the company, we think about in three buckets, which are public companies, your Google's and Facebook's and so forth, to your late stage companies, late-stage startups, the most late stage of which an example would be Stripe. But it's a large variety of serious CDE and above stages startups, which are already making revenue and better not public yet. And your early stage companies, which can be as big as a series A or series B, with substantial funding or they can be seed or pre-seed, pre-product market fit, even they are your early stage, you know, hope and a prayer companies. And that's okay. At some points, you do want to work for those companies. Obviously, a lot of folks do work for those startups and nothing wrong with it. So that's the first bucket that we want to put in here. The second bucket is your personal area of interest. And I would recommend that you keep this as broad as possible. If you're really open to find out what is out there that you could possibly have an influence in, then just keep it totally open ended. But if you know you have some areas where you want to go, and I know a lot of folks want to go into AI related roles, then of course, this would be where you would put that in the area of interest. Geography is very, very important. A lot of product management roles still are in San Francisco Bay Area, Seattle, and a few other small concentrated metros. So be very specific about where you would be open to working. This is not about where you are, but this is about where you're open to relocating. And any additional preferences, if you have any, I would recommend, again, keeping it broad, you can just ignore this number four if you don't have any of those preferences. What you're looking for is a list of company that is relatively organized by the AI stack ranked. And then you can do a sanity check on it. And we wanted to provide a rationale for why it is in a certain bucket. And if possible, if there's any public information about hiring for PMs, then that would be a relevant area as well. And then you're going to attach your resume. The thing that is trying to do is is trying to leverage the resume content plus your interest, plus the company size to give you something that is actually usable. Usually our list are between 50 and 100, but feel free to go as broad as you want. And my recommendation for most PM candidates these days is to, again, keep your search as broad as possible because I would rather have a lot of interviews. And I'd rather have two or three or more offers at the same time. So you can maximize the compensation and also increase the chances that this is a company that you can stick with for two, three, four years or more. So this will help you make the next steps, which is actually applying for the job. And now we're going to get into, we're going to do an example of this one. This is targeting the resume to a specific role. So we've got our baseline resume. We're going to now leverage AI. We're going to give you an example of this to extract three to five in the key here is non generic must-haves from the job description. I'm going to give you a demonstration of how I would do this live. And then we'll run AI through it as well. The idea is we want to stack rank our bullets so that we can, first of all, bring the bullet that's most relevant to the very top of its area in the resume or even at the very top of the whole resume into the summary. And we, of course, want to spend the most time ironing out the details of that particular line because that's going to be the one that seals the deal for us. And the summary is really going to be the only piece of the resume that we might meaningfully change between jobs that we're applying for. We might do a stack ranking, so reorganization of the bullets, but we're not really going to be adding new bullets because your baseline resume should have already covered everything that you could possibly have. And in very, very rare situations, will you suddenly have to add an entirely brand new bullet? And if you do update your bullet vault, your overall resume with that bullet, and then go forward, you can use that one to continue the stack ranking and customization. And I emphasize this step enough. Way too many people I talk to, I ask them, hey, how many applications did you do yesterday? Oh, I did 100. I said, huh? How did you even have time to do 100 applications? You know, you have to be doing this step of tailoring your resume to the role. The cool thing is we're about to show you how to do it with AI so that you can do it in five minutes. Yes, yeah, huge. Sometimes you just you're just wasting time. If you're just applying with a generic resume, if it does not have these elements, it will be skipped. So you can apply to 100 and it will be completely just waste the entire 100. So let's let's do it now. Let me share my screen here. I just did a search for product manager. So let's go. TikTok came up. I'm not a huge fan because of their lack of work, life balance, but we're going to go with it. Seeing a product manager on boarding experience, let's look at it. And so how I would do this manually is read the whole thing and then look through responsibilities, qualifications and preferred qualifications and try to figure out what the hiring manager was actually thinking when they put this job description together or when they again prompted their favorite LLM to put this job description together. But now what we're going to do is we're going to take all of this and then we're going to hang on a second. We're going to put it into our prompt and the prompt that we're using, the prompt. I will give you my baseline product management, job description. What we're looking for is you extract the top three to five non-generic, ignore vague terms like team player or excellent communicator. We cannot map for that and people make a ton of mistakes when they think they have to hit every single element in the job description. And they're using AI to actually create a bullet that speaks the language of the job description, which is ridiculous because you're basically solving it for another AI, but I need you to solve it for the human in the loop, but leverage AI to do it. So, ignore a team player or excellent communicator. Ignore standard PM job requirements. Focus on unique elements that the hiring manager is likely specifically looking for in an ideal candidate. Rewrite my summary. That's our focus to highlight quantified impact that proves those must house or adjacent areas. Now, one thing to note here is that we are trying to keep our AI as honest as possible, but as you know, things happen. So, it's going to be on you to sanity check this stuff and make sure that it doesn't just make things up because sometimes my solution for this is I recommend to apply for role. If you personally think you're at least a 50% overlap with what the role actually calls for, that way you're never going to be in a situation where your AI actually needs to make things up for it. Again, we're going to use the same bullet style. We're really going to return only the updated summary and revised bullet list for the most recent one to two roles. Remember, we're not rewriting the whole resume. We're just focusing on a top half page. So, our AI is only going to revise our summary and the bullet list of our top one or two roles. We then paste the baseline product management resume and the job description. And let's do it now because we love doing things. Here we go. Okay, I will give you paste baseline resume. Let me pull that. I'm just going to show you the full problem we're done. All right. So, this is the prompt. This is our prompt. All right. I'll give you my baseline resume. Here's my baseline resume and I give it the baseline. And then here is the here the job description. Here's the job description. Let me copy and paste everything from there. And here's where we got the five top five non generic must have account infrastructure and scalability owning improving account system. So identity authentication data model reliability. Okay, cross-functional orchestration. So, this ordinarily I would throw into the general PM skills, but because you're you're aligning in for application and policy stakeholders, I will allow it. And then technical depth and platform level growth. So, we got large ecosystems right. Tiktok that makes a lot of sense. And then managing PMs or leading multi team. So, large cross-functional growth, large cross-functional teams. All right. So, how do we update our summary? We got group PM 10 years trying global platform growth reliability across ads privacy and app ecosystems. That's basically it like in my case, of course, it was a little bit easier because I have the experience, but we we basically give it exactly what the recruiter wants right up front. The the thing that we would add to this is Google. So, the word if you have recognizable companies, we would add to that. For instance, like X Google, comma X Google here, led cross-organishatives improving account security, data access compliance for two billion users scaling a platform revenue 50% here over a year to 3.5 billion. So, actually, I'm not a big fan of saying proven record, proven record, but we would leave building PM orgs and infrastructure and we would add an additional impact here of how many people I actually led or how many cross-functional people if you didn't lead an actual PM team. So, this would be what are we trying to do? We're trying to just hook the reader and the reader is our recruiter who's looking for these things. What do they say as their preferred qualifications? Let's sanity check it now. Experience in building and growing diverse content products. Experience in enterprise level platform. The main thing to take away here is that the summary is going to be your biggest hook. That's going to be the thing that's going to get the reader. Let's move on. All right, so that's that is targeting the resume role. Now, we're going to get to the next very important element, which is the outreach and people that just do the application. I'm sure you've heard of people telling you I can't get a call back. So, I do applications, but I can't get a call back, right? Yeah. So, outreach is the name of the game. Every I run office hours every Wednesday for the entire PCA member group when we have hundreds of PMs there now. And we start out with like, what are your wins? And all the time we get like, I actually got a callback from a cold application. And I got a callback from networking and from actual outreach. So, it's always half-half. If you're only doing cold applications, of course, you're missing out. If you're only doing networking, you're probably also missing out. You got to do both. You got to combine application without reach to just maximize your opportunities. And we're going to do AI leveraged outreach, which no, we're not going to be telling AI to personalize the message and come up with something ridiculous, not at all. We are going to, again, focus on the fundamentals. We're going to only apply to just a few of recent roles. And we're going to find the people and actually get in touch with them. And we're going to do it quickly. We're going to focus on hiring manager, recruiter, and senior product leaders. And we're going to try to get their email through contact out posts or some other places. And then we're going to use AI to draft a refined message. I'll give you a template. And, again, we're going to give you some keys to common sense on this one. We want to target both email and LinkedIn in an ideal case. Also, if you can use superhuman or something else where you can get a read receipt, it's going to be even better because it's going to tell you when your emails are being successful. Again, your focus always, always on them, not you. Your three bullets tied to their top means and how you can actually help. Keep our messages short, one intro line at the top, three bullets and a CTA call to action, get in touch, forward to your recruiter, whatever we need. And then you're going to follow up two day, three day, five day, usual top of funnel, follow up work. If you've done sales, you know what I'm talking about. Yeah, you have to follow up. Don't expect that first message to deliver success all of the sudden. Just think about how you yourself view email and how many emails you miss. And when you get that pain, hey, friendly pain, boom, now I got you. So here's the prompt. I'm going to give you the resume. I'm going to give you the job description again. Again, I want you to find non-generate problems, same exact idea of views last time. I want you to write outreach messages for different target people. And again, we give it all the instructions that we just talked about. The short CTA is going to be forward your four, please forward this resume to your recruiting partner or connect for a chat if it is the recruiter already. So recruiter, hey, let me know when you have five minutes to chat. If it's anybody but a recruiter, please forward this resume to your recruiting partner. Subject line we're going to, if you're doing email, revise separately. We're going to keep it under 60 characters. It has to act as a hook for the recipient. And that's what we're going to get our AI to do. Here is a template. Very straightforward. Again, subject goes straight at it. Great fit for your role. Why? How you can help. What are we using? The impact that you've had. The names, organizational names, and the areas of your experience that you can highlight. In my example, X Google adds PM. Big revenue. Great fit for this role. That's what we want from a subject line. And use whatever you've got. If you've got great educational background, use that. If you have recognizable names, use that. If you deliver a lot of revenue or a lot of users, use that. Use whatever you've got to get them to actually open the email. Of course, it has to be, it has to be the truth. Otherwise, it's clickbait and it won't work. How do you find the emails? Finding the email. So we can actually do this together. My favorite way and we talked about this a little bit here is that on LinkedIn, all product managers, all recruiters, product recruiters, and leaders are all there on LinkedIn, which means that a lot of times your first source is going to be who is written a post about a particular job. So if you've got alerts set up, for instance, for a new job, then if you get it, the moment you get it, you can do a surge for who is writing about it online or who's commenting about it. You can very quickly find either the hiring manager or someone else that's related to this role that is saying, hey, my product team is looking for a PM. Please let me know if you're interested. They will actually just ask for it a lot of times. Now, what do people do? They will go and comment below and say, hey, I'm interested. Of course, that's not what we're doing. We're just trying to find the names who are the people involved. So you find the names you can use contact out or whichever email provider you want, but contact out as an easy LinkedIn extension. It will give you, I think, five emails for free per day and you can pay to get more. They're usually pretty accurate. A lot of times, those common sense emails and you're either going to get them from the post or you're going to do your own search, you're going to find who either just put in the company name and then product role or recruiter and just browse them and you're finding anybody who you think is adjacent and then you email them. The cool thing about this is that your email is so short and so targeted and so aimed to actually solve a problem for them that when you ask, if this is not you, please forward this to your recruiting partner or forward this to the recruiter, to the relevant recruiter. They will just do it. It's a very easy lift for them. You're not asking for a coffee chat. You're not asking to grab 15 minutes. You're just saying forward this because I can solve a problem. That's it. That's the whole secret. Nice. Can you show us what contact out looks like and maybe an example of using the AI to prompt it? All right, so let's go to jobs. Well, this is also an example of product manager in San Francisco and we will look at and also this is how I recommend everybody do it most recent. Try to focus. You should be doing this pretty much every day so you can focus on the last 24 hours. Don't worry about any of these. I wouldn't worry about this. If you are looking for hybrid or remote roles, I would do a separate search about it and then I would just clear everything else, show results and then just go from the most recent. This is 39 seconds ago into it. Senior Product Manager Payments Platform. So this is super, super fresh. We can actually just do a search now for senior product manager payments. So let's just go here and type in senior product manager payments into it and just see what happens. And we're looking, we are looking at posts. Senior staff right away. So this is an example of this. This one just came out 40 seconds ago, so it's not that relevant, but this is a literal example of how I would do it. This person is hiring. I'm hiring. I'm looking for strategic level of lot. Senior Staff Product Manager into it Academy. This person for all my product and adult learning friends, the job looks amazing. So this is a person that's reposting. We can go and look at this post. We will now look at Janani and we can turn on the extension contact out. Oh, where is it? I switched to Arc so we don't have it. Let me see if... Yeah, contact out is a little amount here. Well, that's the thing. You can watch me log in to contact. All right. It's funny because like the thing about trying to teach 250 people at the same time, as I don't, we got CSMs that actually work people, walk people through all of these step-by-step. But yeah, so you see this? All right. And that gives you her email. That's amazing. And now can you show us how you would customize the message with AI? Yes. So now we got to go and we look at the job or at the job. So she's hiring for this role. All right. Into the Academy. So we will... You know what? We'll just take from here. And then we're going to give... I'm not sharing my screen with this as well. Okay. All right. So we gave... We gave the prompt. I'll give him my favorite resume and... I'll give him my tailored resume in the job description. Again, we're identifying the key points. We're going to write the outreach messages. Here I pasted my actual resume. Then I pasted the job description that we got when we were browsing. And then we identified the four non-generate problems, scaling app-powered learning, operationalizing skill-based learning journeys, driving... Again, this is very frequent cross-business unit alignment investment, integrated antenna ML capabilities. So pretty good. If we nail these four or close to it, we're going to do well. I don't know how well my background will line up with this. Let's see what we come up with. Hiring manager message. Scaling Andrew and learning cross-50,000 experts. Hi. Name. I'm a former Google group. Yeah. I'm leading three and have done in product lines. Privacy systems, impacting two billion users. Interested in senior staff into it, Academy role. And then our three bullets. And again, you can use two bullets, three bullets, but try not to go over and try not to make them long. Scale global app, add platform, 50% over your revenue growth. Bells you in for future innovation. ML-based compliant systems, two billion users. This was actually in Google Play stuff. So actually, that's actually true. I forgot about that. And I have to double-check this stat. I think that is straight from the resume. Led 7PM org delivering cross-product infrastructure, line of company log. It has a little bit vague. So I might tweak this a little bit, just not to make it generic to this company level of KPI, but I do like this thing where we're leading a 7PM org. And then if this is to a recruiter, then yes, would you be open a short chat? How I can help scale. Very straightforward. This works. Now, if it's a hiring manager, I would actually instead say, please, if this is interesting, please forward this email to your recruiting partner. That's it. If it's a recruiter, the AI messed it up. The recruiter message should be, please reach out. And the hiring manager is forward my resume to the hiring partner or advise the next steps. Very straightforward. Very short. With a good, a good subject line, I would probably drop a recognizable name or impact right in here or make it relevant for the role. Other than that, this is perfect. Awesome. Okay, this referral request, which obviously now is a good time. You can easily get referrals for a lot of roles because they're incentivized, and they want to solve the problems that the hiring manager laid out. I think I'd have a big impact in this role at this company. Please forward this to whoever's the next person in the chain. Here's where I'm a good fit. Again, keep it super short. Your experience, your skills, things you've delivered. Happy anniversary and questions. Here's an attached resume PDF. That's it. Very easy. There's a lot more theory on networking that we do inside PCA. And like, it's a whole, it's a whole rabbit hole. We're not going to go down there, but I do encourage you to look for referrals as well. Let me just do my soapbox thing now. This is the golden age of networking that we're in right now. Everybody in business is on LinkedIn. And certainly everybody in product. And certainly everybody in AI and product is on LinkedIn. So you need to optimize your profile. Please think for not just the things that you're putting out there, but also for people that are visiting your profile. Please also send connection requests. You can send up to 30 a day. Just send them to recruiters, product managers, leadership, senior leaders. If your profile is optimized, you're going to get a request from a quote unquote naked connection request, which is non-personalized. You just send an invite. And if your profile is good, they're going to say yes, a lot of the time. So you can just build a network passively, just without doing anything except clicking connect, connect, connect. It opens up future connections, which will reveal themselves in terms of utility, sometimes down months or quarters or even years. Commenting right now is the thing to do to get the most impressions and doing comments can drive a lot more impressions than posts. So if you don't post heavily, please do comment. Of course, don't this is one place where you should not use AI. Please comment your own stuff so you can actually get visibility interaction with other users. All right, that's it. Are we going to fly through behavioral interviews? Yeah, we need to talk about interviews because we've just helped people use AI on every step of the job search, creating your baseline resume, creating your company list, tailoring your resume, creating outreach connections and turning those into referrals. Now you need to ace the interview. How do you ace behavioral interviews? That is the next step indeed. So behavioral interviews are every interview just to define things a little bit where you could be asked a question, how do you think about this concept? How do you do this thing? Or how have you done this? Or like in the Amazon style, tell me about a time that you blank. So those are all behavioral. Those are like how would you behavior? How have you behaved? Or what do you think? And we really need you to practice for these. And of course, AI is going to be the best place for you to create the stories with which you are going to then nail your behavioral interviews and also give you a bit of a sparring partner, an interview partner. So what do we need from our behavioral interviews? In general, the thing that I want you to get to, this is our method of answering behavioral questions. I'm going to quickly go over it. Hook, which is again, you got to hook the listener. Whenever you're asked a question, you've got to come back with a hook. Some reason that the listener should be excited or curious about what you're going to say. And also that you promised that you're actually going to answer the question. Your principles, some of the reasoning behind why you think the way that you do or just state the shared approach or principles that you use, the things that you actually did or the things that you would do, and the results that you've delivered or that it has led to your action. And finally, learnings, anything that you have acquired during this process or anything that then informs some of your principles some way of wrapping up the overall story. Now, here is how you're going to build your stories. First, you're going to do the written content. Now, you've already done a lot of this when you did your initial work that we talked about those 32 questions. And it is in that doctor. So you can now leverage AI in order to actually write out stories that answer a particular question. I'm going to share with you a few top questions, but you start out by taking the entirety of the content and creating a story around it. And we just start with a very simple story, just a couple of paragraphs that you would write out. Or you would say the prompt would be, I want to answer a few questions. I want to answer the question of how, tell me about a time that you launched a product or tell me about how you deal with conflicts or tell me about how you align stakeholders. And then you can prompt AI to give you a. Give you a thread to pull on this episode is brought to you by linear. You know what's broken about product development right now? It's not the coding. We've got AI agents for that. It's everything else. The planning, the feedback synthesis, the endless context switching between tools. You're drowning in PM busy work while the actual product work suffers. Linear started as that issue tracker that engineers actually want to use, which, let's be honest, is pretty rare, but it's evolved into something way more powerful. Your entire product development hub. Their new AI features are a game changer for PMs. Product intelligence automatically surfaces insights from customer feedback and support tickets. No more manually combing through hundreds of reports and tickets. And their AI agents will help draft PRDs, scope projects, and handle all the status updates nobody wants to take care of. Product teams from OpenAI to Versel use linear to build complex products at speed. There's a reason they call it magical. See for yourself at linear.app/akash. That's L-I-N-E-A-R-A-P-P/A-A-K-A-S-H. Like a template to start with, knowing everything you've already put in your original, in your initial large write-up when you did all that work with the questions. Then when you've got the written content, you can fill in the blanks. You can fill in the details, some of the metrics, maybe a little bit of the structure. And only after you've written it down and it looks good in a written format with you, then do you actually practice your delivery? So you don't want to jump straight into delivery and start to memorize something. We don't want that at all. We want you to first work so that it's a cohesive story and it maps to those general five principles of how to approach behavioral. At that point, you actually start to practice your delivery because written content and the way that you communicated verbally are two different skill sets, two different areas for you to practice. Then the third component is, now you've got to put a time limit on it. You usually only have a minute or two or three at the most to actually deliver these stories and answer these questions. But you don't want to start by saying, "Hey, let me try to do a one-minute story because you're going to fail." And we don't want that. So start with the written content, then layer in the delivery and recording yourself and only then try to put a limit on it and see if you can shave off elements that are not necessary. This is the general approach. You're going to refine it at each stage. Cut, cut, cut. Anytime something is in there that does not add to the story, does not add to the five elements that we just discussed, just get rid of it. And anything should be cut. It's like, I would have written a shorter letter if I had more time. That's what this is. Okay. Now, how do we leverage AI? So here's the whole workflow. Let's go through it. All right. So we've got the five-step structure that I told you about. We want to draft the story in a five-step structure. You're going to feed the story and the job description and the company values because those are the second and third important elements of what you need to really do well on the behavioral questions. You're, think about it this way. Your story is the you component, but each company has their own problems they're trying to solve and values and their general ways of doing things. So we are trying to integrate all three of these. All right. So for case interviews, I'm going to go over some of the most important like, so for case interviews. Again, think about it as an interviewer. You've got a certain rubric that you have to adhere to and then you have to give your response. After the interview, you have to say, okay, how did this candidate do on this rubric and on this rubric element? And based on that, how do I rate them? Higher, strong higher, do not hire, meaning higher. What do I say? And how do I back it up? So we're going to back this up and we're going to synthesize it and check our answer against rubrics for most common questions. We keep going to a lot of detail here. Let's just do the fundamentals. So here's the rubric that we're going through. And this is the rubric that we actually leverage and a lot of top tech companies use as well. Structure thinking, number one, we want to be clear, we want to be logical, we want to go from step one to two to three user focus. We want to be thinking about the user. We have to be empathetic. We have to really think deep about the user. Product sense is more about the solution inside, but also how the business is captured, how the business captures value back some of the value back that it delivers to the users. Priorization, always a big part of the product management interviews. We want to know how do you make decisions, how do you prioritize, how do you stack rank, what are the principles that you base it on? Communication in general, this is clarity, this is verbal communication, this is structure of what you're delivering and creativity as a general bucket, which can apply to pretty much any of these areas. So how do we want to use AI? We want to take any of the common questions. And this is design product X, improve product Y or redesign product X for segment Y. It's variations of that. You can find a question, plenty of different questions that are asked at most top tech companies, ask very similar questions. And you want to give your answer, I would again start from a written response. So when you are practicing a question, take a question. Write down the answer, feel free to take as much time as you want, 20, 30, 40, 50 minutes and just write it out so that it's clean. Don't go back and edit and refine, but just try to write as if you were speaking, but don't add the extra complexity of actually speaking. So write first and then send this over to your favorite LLM with this rubric and these instructions. After giving me a score on this rubric one through five, give me this specific statements, the exact phrase that I used or that I wrote down that is actually weak or inadequate, explain to me exactly why this was the case. Give me a better approach. And then at the end of it, give me two areas to focus on. And this is actually very similar to what we do inside PCA, when we run you through either a 101 coach or group session or even an async assignment that we have you do. We want to know what went wrong, why and how to improve it and then what you should focus your attention on next. Treat this a little bit with a grain of salt. AI is always going to give you a better approach. You can do this on an infinite loop. So don't worry too much about the specifics of it, just the direction on it. What like what is the reason why this failed and what would be the better approach just to follow it directionally and then repeat, repeat, write down another case, write down another case and then get your responses. Important, you have to feel pretty good from a self-assessment perspective about nailing these rubrics. If you were assessing yourself before you move on to the speaking face because once you're actually speaking this stuff, the answer as opposed to just writing it down, it adds a ton more complexity. You can still follow the same exact logic by testing yourself on the spoken component when you move over to that by using either a speech to text of your favorite one and then giving the feedback again to the AI. All right, that's actually pretty good. So what about the execution and analytical interviews? Do you have to change the prompt? So there's a slight change when you're dealing with execution on analytical questions such as root cause analysis or trade offs estimation, go to market, a prioritization question. It's going to be slightly different. We again give it the rubric and we're going to ask it to give us a little bit of instruction. So it's going to be slightly different. We added a few things, elements here. So for the analytical rigor, we want the right metrics actually. We want KPIs that make sense in a case of a North Star metric. We want there's no necessarily right answer, but there are plenty of wrong logic, wrong logic of getting to an answer. And remember, especially in analytical, the thing that's being evaluated is the way that you think not the actual final solution that you come up with. So the analytical rigor here is important and the the structured thinking is especially important here. So similar assessment, rate me on the rubric, give me where there was weak logic, where did my flow not make sense, where should I've explored deeper. And if there's a better framework or approach to using certain situations, again, we end off with top two focus areas. The thing to remember here again is start with written, then move on to spoken and only then add a timed restriction. So do run yourself through a case study with only 25 minutes on the clock and then see how well you do. You might just run out of time at the end. And so the prompt will tell you, hey, you didn't answer the question. You didn't actually get to the final answer, which can be an inappropriate use of time. So work in stages. That's what I've noticed is that mismanagement of time is actually one of the biggest things people do in the case interview. So don't over dwell on the written phase. Make sure you get to that actual practice phase. Absolutely. It's just that we want to, there's a fine line here because if you just do the time, then you're going to lose the content. And if you just do the content, then you'll mess up on the time. Amazing. So we've been talking about $700,000 plus jobs, but a lot of PMs, they're just at $140,000. Is it actually realistic for people to land these high paying doms? In short, yes, I myself was a PM that was making probably 140, 150, maybe up to 200. And then I made the jump directly into Google. So it's certainly possible. And many, many of our clients have done the same. Now, it is a process and it is a complicated process to actually get there. But there's a surprisingly small number of actual requirements. There's a lot of made up requirements that people have in their head about what it takes to become a $700,000 PM. And a lot of times people just don't want to believe that these things are possible. But yes, it is definitely possible. You heard it from him. He's done it for tons of people like you. If you missed any step in this roadmap, just rewind, get it back. Find Alex on LinkedIn. He has an amazing LinkedIn presence, find him on YouTube. Can hit me up on LinkedIn too, although I'm not as good with fine DMs as Alex. And we will see you guys in the next episode. Bye, everyone. Thanks very much. I really hope you guys enjoyed that episode. It would mean a ton to me and the team. If you could please subscribe on YouTube, follow on Apple and Spotify podcasts and leave a rating and review. Those ratings and reviews really help grow the show and help other people discover the show. And they help fund the production so that we can do bigger and better productions. Can't wait to share the next episode with you. Until then, see you later.