Landing your First Machine Learning Job: Startup vs Big Tech vs Academia

This guide is for early-stage Machine Learning practitioners who’ve just graduated from university and at the moment are in search of full-time roles within the Machine Learning field. A lot of the experiences shared here come from corporations and universities based within the United States. Bear in mind that that is blog post is inspired by my personal journey, so not all the pieces may apply to your specific case. Use your best judgment and revel in the read. 🙂

, I had just accomplished my Master’s Degree in Physics of Complex Systems and Big Data on the University of Rome, graduating with full marks. My master’s degree progressed fairly easily, and through my studies, I accomplished two internships and various practical Machine Learning projects. I also accomplished my Master’s Degree in 1.5 years as an alternative of two. I believed my master’s degree was a transparent indication that I used to be able to working and succeeding. Seems I used to be not only “improper”; I used to be terribly improper.

Unfortunately, the power to “sell” your skills and get through the recruiting process is a skill in itself. Throughout the years after my Master’s Degree, I needed to learn a set of soppy skills and techniques that weren’t taught by my university classes, but they were crucial to finding a job. Specifically, I learned that finding a job for a Research Lab/University* is totally different than finding a job in a Startup, and finding a job in a Startup is totally different than finding a job in a Big Tech Company.

After ending my PhD, I went through several hiring processes and ended up with offers from three very different places: a , a , and a . Getting noticed, passing the Interviews, and getting these offers wasn’t easy; it was the results of several mistakes and good decisions I made along the best way. This text desires to share my experience in order that an early-stage Machine Learning practitioner can shine within the job-hunting process, whatever path they determine. Before occurring, I’d prefer to be clear on two points:

1. This text is just . While I do consider that sharing it could possibly be helpful to a variety of people, please consider what applies to you and use your best judgment.
2. This text is just not a “” type of article. It is supposed to be a no-fluff, no-hype, specific guide on what to do to be able to reach the job-hunting process for Machine Learning roles.

To be able to get your Machine Learning job, there are 4 specific steps to follow:

In the following chapters, I’ll break down each of those 4 steps so that you’ll have a transparent idea of approach every stage of the method. Let’s start! 🚀

* Throughout this text, once I seek advice from a “Research Lab,” I don’t mean R&D roles at corporations like Google or Meta. As a substitute, I’m talking about research positions in academic institutions, national laboratories, or public-sector research centers: places like MIT, Lawrence Livermore, or university-affiliated research groups. These roles are typically more focused on publishing, grants, and long-term scientific contributions than on product-driven innovation.

0. Do your homework.

Before discussing the 4 principal points of landing a job, I consider that there may be a crucial point to make. The Machine Learning job market is competitive, and facing it with no solid understanding of linear algebra, statistics, algorithms, data science models, and strong coding skills is largely not possible. Recruiters can quickly tell when someone is bluffing, and it’s surprisingly easy for them to identify whenever you don’t know what you’re talking about. I strongly suggest not attempting to cheat the method.

The remaining of the guide assumes that you just have already got a powerful Machine Learning foundation, each theoretical and practical, and that your coding skills are in fine condition. Now, let’s return to the method.

1. Know the job market.

1.1 Introduction

The job hunting process starts with asking yourself some questions. Which path is best for you? Are you in search of ? ? Or are you trying ? This a part of the article explains the difference between these three sectors so you possibly can have a clearer understanding of the job market and you possibly can make your decision.

1.2. Working In a Startup

Once you work in a startup, you normally wear multiple hats. You’ll handle a variety of things, like MLOps, Model Deployment, Data Acquisition, and all of the software engineering that’s in the center. You may also learn communicate with investors, approach problems from different angles, and sharpen your soft skills in ways in which more structured environments rarely allow. For these reasons, startups are frequently considered an awesome place to begin your profession.

The downside is that your employment in a startup is far more unstable than the one you’ll have in big tech corporations or research labs. The rationale is straightforward: startups are more susceptible to failure. In 2021, Harvard Business Review estimated that more than two-thirds of them never deliver a positive return to investors. In January 2024, Stripe confirmed that greater than 90% of startups fail. Even Growthlist tells us that lower than 50% of startups manage to survive.

Startups also normally offer lower salaries than Big Tech. Wellfound tells us that the typical salary within the USA is barely below $130k/yr. Given the lower salary and the abovementioned risks related to a startup, they sometimes give you a reasonably decent equity package (0.5%-3% of the corporate).

1.3. Working In a Big Tech Company

In contrast to startups, employment in an enormous tech company, equivalent to Google, Meta, Amazon, Apple, or Microsoft, offers significantly more stability and structure. These corporations have established business models, mature engineering practices, and the resources to support large-scale, long-term research and development. From a compensation perspective, big tech corporations are among the many highest-paying employers within the industry. In accordance with Levels.fyi, entry-level Machine Learning Engineers (e.g., L3 at Google or E3 at Meta) typically earn $180k to $220k/12 months in total compensation, including base salary, bonuses, and stock options. These corporations also offer generous advantages, including wellness stipends, retirement matching, parental leave, and internal mobility opportunities.

A thing to contemplate about working in an enormous tech company is that the “structured” setup of a Big Tech Company permits you to grow in your specific area, but it surely won’t be one of the best when you prefer to wear multiple hats and learn from multiple areas. For instance, when you work on the LLaMA team at Meta, it’s highly unlikely that you just’ll ever interact with the teams constructing the corporate’s virtual reality products. Your focus will probably be deep, but narrow.

1.4. Working in a Research Lab/University

Okay, on this one, I would like to be brutally honest. For a similar seniority, academia pays you way lower than the industry. Even very successful professors with great publications would earn far more in the event that they joined the board of an enormous tech company, for obvious reasons. Even when you develop into a professor in Machine Learning, you’ll still earn way lower than you’ll as a Senior Machine Learning Engineer (take a look at the report from HigherEdJobs). On top of that, the tutorial world may be extremely competitive, as the tutorial track for prestigious universities puts you in direct competition with among the most driven and talented researchers on the planet.

For those who are still reading, that implies that you really like academia. And if that’s the case, then it’s price exploring the opposite side of the moon. Because here’s the reality: despite the lower pay and intense competition, academia offers something incredibly rare: mental freedom. Within the U.S., you possibly can construct your personal lab, apply for grants, propose daring research directions, and explore questions which will don’t have any immediate industrial value. That freedom is something industry often can’t offer. There are frequently two sorts of Machine Learning research: you possibly can either apply Machine Learning to existing research problems or perform research specifically on Machine Learning, creating latest algorithms, neural networks, and optimization techniques.

1.5 Summary

A fast comparison between the three settings, summarizing what now we have said before, may be present in the image below.

I would like to reiterate an idea. Let’s say you don’t really know if you must work in a startup, an enormous company, or a research environment. Possibly you had a few startup experiences, but you don’t know the way life can be in an enormous company or a research laboratory. Is it bad?Initially of your profession, whenever you’re still figuring things out, an important thing is to start. Gain experience. Try things. You don’t have to have all of it mapped out from day one. It’s nice to not know exactly where you must find yourself.

2. Stand Out

2.1 Introduction

An important thing to fret about is stand out. You will discover yourself competing with a pool of very well-prepared people, and by some means you will probably be the one who stands out. The goal of this a part of the chapter is to offer some techniques so that you can be appealing within the Machine Learning job market.

2.2 Your authenticity is your best weapon

I’m going to say something which will sound a bit of weird, as we’re all Machine Learning enthusiasts: please don’t blindly trust AI to generate resumes/cover letters/messages to recruiters. Let me be more precise. It is totally alright to ask ChatGPT to enhance your “summary” section of the resume, for instance. What I’m suggesting is to try to change ChatGPT’s text to make it personal and let your personality shine. It is because recruiters are getting uninterested in seeing the identical resume in 10,000 candidates. Your authenticity will distinguish you from the pool of candidates.

2.3 Construct a very good resume

The resume is your corporation card. In case your resume is messy, filled with columns, filled with meaningless information (e.g., pictures or “fun facts”), the impression the recruiter could have of you it’s that of an unprofessional character. My most successful resume (the one which got me probably the most job offers) is that this one:

Easy, no picture, no fluff. Each time you write something, attempt to be quantitative (e.g. “improving AUC by 14%” is healthier than “improving classification performance”), and make the formatting easy such that you just don’t get filtered out by bots. Avoid putting information that is just not related to the job you might be applying for, and check out to not exceed one page.

2.4 Construct a portfolio

Certainly one of the toughest parts after graduating is convincing recruiters that you just’re not only someone who studied the speculation, but you’re someone who can construct real things. The very best solution to accomplish that is to select a subject you might be keen about, create your synthetic data or extract it from Kaggle (when you need a dataset), and construct your Machine Learning project on top of the dataset. A wise thing to do is to construct projects you can link to a particular recruiter. For instance, when you’d prefer to work at Meta, you could possibly start a project about using LLama to resolve a real-world problem. They don’t must be paper-quality pieces. They simply have to be charming enough to impress a recruiter. Once you’ve gotten the code, you possibly can:

1. Showcase the project on a blog post. That is my favorite solution to do it since it permits you to explain, in plain English, the issue you needed to face and the way you managed to resolve it.
2. Add it to your personal GitHub Page/website. This can also be excellent. One could argue that a GitHub page gives more of the “software engineer” vibe, while a blog post is more “recruiter-friendly”. The truth is that each work thoroughly to face out.

Also, each time you publish a project, it’s an awesome idea to share it together with your LinkedIn network. That is how my portfolio looks.

3. Get the interview

3.1 Introduction

Okay, so now we have our resume, and now we have our portfolio. Which means that if a recruiter looks at my profile, they discover a very well-organized portfolio, they usually can reach out. Now, how will we actively search for a job? Let’s give a glance.

3.2 Looking in person (Profession Fair and Conferences)

Throughout my profession, the one way I discovered full-time opportunities was through my network, either my virtual network (LinkedIn) or my in-person network (through people I knew and profession fairs). For those who are still in university and you might be in search of startups/big tech corporations, don’t sleep on profession fairs. Prepare 1-page resumes, study the businesses beforehand, and rehearse your one-liner introduction so that you own the conversation from the start. For instance:

Again, don’t feel discouraged when you leave the profession fair with none immediate job interview. I left the profession fairs with no interviews and, after just a few months, I began receiving messages like these.

For those who are in search of Research Lab opportunities, your academic advisor is one of the best person to ask, and one of the best places where you possibly can actively look are the conferences where you present your work. After the conference, invest a few of your time in talking with presenters and see in the event that they are hiring postdocs or visiting scholars. It’s normally not vital handy your resume, as they are usually not technically HR they usually can evaluate your research by talking with you, reading your paper, and listening to your presentation. Remember to offer your email, and collect researchers’ emails and business cards so you possibly can reach out.

3.3 Looking online

It is a secret-not-so-secret routine I used to seek out jobs online.

0. (On LinkedIn only) On the LinkedIn search bar, seek for “Hiring Machine Learning Engineer in [Location]” and filter for “” and “” (see screenshot below). You will note the contact of the recruiter posting the job application, and you will notice the job application before LinkedIn promotes it within the job section.

1. Apply for the position with a tailored cover letter (not greater than 1 page). By “tailored”, I mean that it is best to have a look at the corporate’s website and find overlaps together with your work. You must explicitly mention this overlap in your cover letter. You possibly can prepare a template cover letter and tweak it based on the particular application to make things quicker.
2. Find the recruiter who has posted that position (when you can)
3. Send them a message/an email, saying something like (when you can):

“Hello, my name is [Your Name], a Machine Learning Engineer graduating from [School]. I hope this message finds you well. I’m writing you this message regarding the [X] job post, as I feel I’m an awesome fit. Througout my profession I did [J, K (make sure J and K are somehow related to X)]. I might like to borrow quarter-hour of your time to debate about this. Please find my resume and porfolio attached [Attach Resume, Attach Portoflio/GitHub]” + Send Connection Request

For those who are applying at startups, more often than not you possibly can directly discuss with the CEO of the corporate. It is a huge plus, and it helps speed up the hiring process by lots. An analogous thing happens in research labs, where more often than not you possibly can talk directly with the professor of the department that may eventually (hopefully) hire you. Please, keep this in mind. 9 people out of 10 will leave you on read. Possibly even 19 out of 20. The one thing you wish is one one that is willing to present you a shot. Don’t get discouraged and trust the method.

I strongly discourage using software to generate 1000’s of canopy letters in seconds and apply to 1000’s of jobs. The of your application will probably be terribly low: your application will probably be exactly like the opposite 1000 filled with em dashes job applications. Give it some thought. Why would the recruiter select you? Would you select yourself when you were the recruiter? 20 good applications a day, with a tailored cover letter and a customized message to the recruiter, are way higher than 1000 AI-generated ones. Please trust me on this one.

4. Pass the Interview

4.1 Introduction

Okay, so there may be a recruiter who appears like you could possibly be a very good fit. How will we get to the stage where they send us the job offer? Let’s give a glance.

4.2 The Startup Interview

Defining the startup interview is incredibly tough since it dramatically will depend on the particular company. It’s fair to assume coding exercises, questions on your previous work experience, and an off-the-cuff speak about your work ethic, where they struggle to see when you are “fit” for the startup world. From my experience, the startup interviews are frequently pretty short (one/two rounds). The very best solution to prepare for them is to review the startup mission and check out to seek out an overlap between your past projects and the startup mission. Also, startups are likely to close this process in a short time, so when you are interviewed, you might be probably on a really short list of candidates. In other words, it’s a particularly good sign.

4.3 The Big Tech Interview

Okay, this one is long and hard, and it’s best to be prepared for a troublesome process. You sometimes have a principal recruiter who helps you prepare and provides you advice. Throughout my experience, I even have all the time found amazing people there. Remember: nobody is there to see you fail. You possibly can expect no less than 2 coding rounds, no less than 1 Machine Learning System Design round, and no less than 1 behavioral round. This process normally takes between 1 and a pair of months to finish. Sadly, getting interviewed is a very good sign, but it surely is just not a sign. Rejections occur even on the last round.

4.4 The Academia/Research Interview

For my part, that is the simplest of the three. If you’ve gotten studied the research project enough, you might be probably good to go. Attempt to approach the interview with an open-minded approach. More often than not, the professor/interviewer will ask you questions with no precise answer in mind. So don’t panic when you are usually not in a position to answer. For those who are in a position to provide a somewhat impressive and plausible suggestion, you’ve gotten already aced it. I might not expect greater than 2 rounds, possibly the primary one online and the second on-site. It is rather necessary that you just study the research project beforehand.

4.5 Learn how to prepare

Each round requires a distinct type of preparation. Let’s speak about it.

Concerning the coding round. I’m not being paid by LeetCode, but when you can, I strongly suggest getting the premium version, no less than for a brief period of time. Search for the standard questions the corporate asks (e.g. Glassdoor), prepare on breadth greater than depth. time yourself, and practice pondering out loud. My impression is that no person asks “easy” questions anymore. I might practice Medium and Hard level questions. With the premium LeetCode profile, you can too select the particular company (e.g., Meta) and prepare for the particular coding questions. Some standard coding questions I even have been asked are binary trees, graphs, lists, string manipulation, recursion, dynamic programming, sliding windows, greedy, and heaps. Once you prepare, be sure you’re making it as realistic as possible. Don’t practice in your couch together with your jazz playlist on. Make it difficult and real. These rounds are frequently 30-45 minutes.

Within the system design round, an enormous company (which I won’t say the name of) really helpful preparing on ByteByteGo. That’s a very good place to begin. There are also a bunch of YouTube videos (this guy is incredibly good and funny) which can be great to see how the interview should look. During these rounds, I even have used embeddings, suggestion systems, two tower networks, latency vs accuracy vs size, suggestion metrics like MAP, precision@k, recall@k, and NDCG. The same old query is about an end-to-end suggestion system, but the particular considerations depend upon the issue. Start by asking questions, keep your interviewer within the loop in any respect times, think out loud, and be sure you follow the hints. This can also be 35-40 minutes.

Concerning the behavioral round. Be prepared to use the STAR method (Situation, Task, Motion, Result). Start describing a situation, say what your task was, what motion you applied to realize the duty, and what the results of it was. Take a look at your resume and consider 4-5 stories like those. My advice is just not to oversell your skills, it’s alright to say that you’ve gotten made some mistakes and you’ve gotten learned from them. Actually, it’s a very good sign that you just acknowledged and grew.

It’s not an awesome sign when you don’t ask questions after the interview. Study your interviewer, follow them on LinkedIn, and prepare some questions for them.

5. The Elephant in The Room

Following the steps of the method, I ended up signing for a Big Tech Company I actually like, on a project that excites me, in Latest York City, which is a spot I’m in love with. Now, it will be very dishonest of my end to pretend that it was easy. I had impostor syndrome, felt like I wasn’t enough and value it, countless sleepless nights, much more days once I didn’t even feel like getting off the bed, and when all the pieces felt pointless and useless. I hope you won’t undergo what I did, but when you are (otherwise you will) undergo this phase, just know that you just are usually not alone. The Machine Learning market may be brutal at times. Remember, you are usually not doing anything improper. The rejections are usually not a mirrored image of you not being ok. You may not be a very good fit for THAT specific company, you is perhaps filtered out by a biased algorithm, they may need canceled the role, or they may need fired the recruiter. You might have no control over those things. Reflect in your mistakes, grow, and do higher next time.

Now, an important thing: it’s essential to trust the method diligently. On the lookout for a job is a job per se. Set a set schedule and follow it. I realize it’s hard, but try to not be emotional, stay rational, and keep yourself aligned with the every day goal. Finding a job is the results of a chronic search, not the consequence of a one-shot trial.

6. Summary

Thanks very much for being with me ❤️. I hope this text is useful to you. Let’s wrap things up with the important thing takeaways from this guide.

• Start by understanding the three job paths: Research labs, startups, and large tech corporations each offer something different. Research gives you mental freedom, but pays less. Startups offer you fast growth but include instability. Big tech pays probably the most and offers structure, but it surely is very competitive and specialized.
• Don’t underestimate your foundation: You would like strong coding, solid ML knowledge, and a very good understanding of math and stats. Don’t skip the basics. Recruiters are trained to catch cheaters.
• Stand out with clarity and authenticity: You have to a clean, well-organized resume, a portfolio together with your work, and an impactful LinkedIn profile. Please don’t use AI-em-dashes-obsessed text. Show your personality, especially in the way you communicate your work.
• Construct strong applications: You don’t have to apply to 1,000 jobs. Use Cover Letters, send messages to recruiters, network a bunch, and create tailored job applications. The work pays off.
• Preparation is non-negotiable: Know what type of interviews you’re facing. The three fundamentals for ML interviews are coding, system design, and behavioral. Prepare accordingly, use the tools available (LeetCode, ByteByteGo, STAR method), and practice under real conditions.
• Rejection is just not failure: You’ll face no’s. You’ll feel impostor syndrome. Remember, one yes is all it takes. Follow your schedule, trust the method, and handle your mental health along the best way.

7. Conclusions

Thanks again to your time. It means lots ❤️

My name is Piero Paialunga, and I’m this guy here:

I’m a Ph.D. candidate on the University of Cincinnati Aerospace Engineering Department. I speak about AI and Machine Learning in my blog posts and on LinkedIn, and here on TDS. For those who liked the article and wish to know more about machine learning and follow my studies, you possibly can:

A. Follow me on Linkedin, where I publish all my stories
B. Follow me on GitHub, where you possibly can see all my code
C. For questions, you possibly can send me an email at 

Ciao!