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How to Craft the Perfect Machine Learning Resume

Published on: Oct 11, 2022
Last Updated on: Apr 1, 2023
By: Editorial Staff
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The machine learning job market is hot. Increasingly, businesses in a variety of industries like finance, healthcare, and manufacturing are hiring machine learning engineers to help build better products, streamline logistics, and increase their bottom line. In 2019, Indeed reported that postings for machine learning engineer jobs had accounted for 179 of every one million job postings in 2018, with the total number of machine learning engineer postings growing by nearly 350% between 2015 and 2018. This increasing demand has translated into high salaries. When ‘machine learning engineer’ topped Indeed’s Best Jobs of 2019 list, the reported average base salary was $146,085. This growth hasn’t leveled off just yet: LinkedIn placed machine learning engineer fourth on its 2022 Jobs on the Rise list, with a salary range of $72,600-$170,000, eclipsing the 2020 median US household income, which came in at $67,521 in the most recent census

The first step to breaking into the machine learning sector is to gain knowledge, skills, and experience through a degree program or bootcamp. But this isn’t enough to land you a job. Attracted by the high salaries and great benefits, a lot of people are lining up to be machine learning engineers, so you’ll have to differentiate yourself somehow.

One essential way to attract the attention of a recruiter is by crafting a stellar machine learning resume. In this article, we’ll give you the low-down on how to get started. After diving more into its purpose, we’ll go over what a machine learning resume should contain, as well as how to make your machine learning resume stand out from the crowd. Finally, we’ll give you some examples of machine learning engineer resumes that successfully helped applicants land a machine learning job.

What’s the purpose of a machine learning resume?

A machine learning resume allows recruiters and hiring managers to quickly understand whether an applicant is a good fit for a machine learning role they are trying to fill. While no aspiring machine learning engineer will get a job based on their resume alone, a resume that demonstrates your experience, your skills, and your success in roles requiring progressively more responsibility and know-how will help you land an interview. As you progress in the hiring process, your resume will remain a crucial tool for interviewers to quickly get an idea of what kind of a candidate you are, how you might fit in, and what kinds of questions they might want to ask you. 

Oftentimes, however, it’s not just a recruiter, hiring manager, or interviewer that will read your resume. Increasingly, before it reaches any human eyes your resume will first be fed into an applicant tracking system (ATS) and read by — you guessed it — a machine learning model, which will crawl your resume for keywords to determine whether you should be shortlisted. Later on, we’ll talk more about how you can optimize your resume for an ATS and ensure your machine learning resume appeals to both human and computer readers.

In addition to its role in the hiring process, a machine learning resume is also an important networking tool. Even if you’re not talking to someone about a job, a resume that lays out your experience and qualifications will quickly give them an idea of who you are and keep you top of mind should they come across any opportunities that might be a good fit for your skills and experience.

Remember, with networking, the name of the game is to help them help you: if someone has to try too hard to understand your experience, or if your resume doesn’t seem professional, they might just decide against it next time they have the opportunity to connect you with one of their contacts.

What should a machine learning resume contain?

Now that we have a better idea of the purpose a machine learning resume serves, we can dive into what exactly it should contain, with the caveat that for a job seeker, specific instructions in a job posting should always take precedence when applying to a machine learning engineer position or other machine learning job. That said, almost any recruiter or hiring manager will expect your resume to include sections outlining your education and training, your experience, and the skills you’ve amassed, especially machine learning skills. Oftentimes a job posting will also request that you include references with your resume. It’s also a good idea to include relevant machine learning projects that you’ve worked on, along with a list of any work or research you’ve had published. We’ll unpack each of these categories below.

Education & Training

The education & training section should include entries for each higher education institution or bootcamp you’ve received a degree or certification from, the year you received it, and, if applicable, what your major was. Usually, a resume will list this educational history in reverse chronological order. If you are in the early stages of your career, it’s acceptable to include your high school as well, though you usually remove it once you’ve progressed. After all, a hiring manager or recruiter will likely be more interested that a candidate who went to Stanford University or Carnegie Mellon University, for example, than Berkeley High School, especially since you are unlikely to get much machine learning training before college. 

The education section is also a great place to showcase any school-related awards and accolades you received, such as graduation honors or honor societies to which you were admitted. Some companies will also request that you include your GPA, especially for an early-career machine learning role or internship.

Work Experience

For a recruiter or hiring manager — or anyone else, for that matter — the work experience section is likely the most important. Listing your previous experience will give someone reading your resume a good idea if you would be a good fit for a data science or machine learning position — good enough, at least, for a ten-minute screening interview. A general rule of thumb is to list out your experience by position, noting the length you were in the role, what your responsibilities were, as well as how you contributed to the company’s success over your tenure. 

When drafting your entries, remember that you are not the end-user of this information, so make sure that your writing sufficiently communicates your work experience to someone who hasn’t worked at the same companies, and might not even have machine learning experience. At the same time, you want to advertise to anybody with machine learning experience that reads your resume that you know what you’re doing, so you also can’t get too abstract. In other words, you want to let somebody know that you have a handle on deep learning while still appealing to someone who doesn’t know a neural network from the Cartoon Network.

 A good way to balance writing for these competing audiences is to ask both professional connections in machine learning and friends and family members who aren’t in the field to read your resume. From their feedback, you can recalibrate to make sure your writing is in the sweet spot between easily understandable and authoritative.


You will likely mention some of your skills in the professional experience section, but the skills section of a machine learning resume is an opportunity to put every programming language, software, and machine learning technique you know in one place, as well as other machine learning skills you picked up along the way. Make sure to include both technical skills, like data analysis, deep learning model design, or big data mining, as well as soft skills like critical thinking, communication, and leadership skills. These soft skills, if demonstrated in your interview, can often be key skills in the eyes of a hiring manager or interviewer that will set you apart from other candidates. 


If you are trying to land your first real machine learning job, there’s a good chance you don’t have much professional experience to speak of, other than perhaps an internship or two. The projects section is a great place to show off your potential by showcasing machine learning projects you’ve designed and completed at school or in your free time. After listing each project, make sure to include a summary of the project, including motivations and key findings. It’s also a good idea to include links to your Github if you have one, and any other materials that would allow your reader to further explore your accomplishments.

For research positions, candidates will also be expected to include publications that have appeared in peer-reviewed journals, as well as conference presentations and other academic contributions to machine learning.

References (Optional)

While not every job posting will ask for references to be included on your resume, it’s important to be prepared for those that do with at least three references and their contact information. In general, try to choose references who have worked with you closely on a variety of machine learning projects and who are not just willing, but eager to sing your praises to a prospective employer. Before you list somebody as a reference, you should also always get their permission: the last thing you want happening is for someone you know to get a surprise call.

What are some ways to make your machine resume stand out from the crowd?

Communicate your experience concretely and compellingly.

When applying for a machine learning job, your goal is to pique the interest of the recruiter, hiring manager, or interviewer. They usually have hundreds of resumes to leaf through, and it’s easy for them to discount those that are vague and don’t paint an actionable picture of someone’s experience. That’s why you should be specific about your exact contributions to a company and quantify these with hard figures whenever possible. Take for example the following descriptions from those seeking a machine learning engineer role:

Applicant 1:

Designed, developed, and shipped 4 machine learning models utilizing deep learning neural networks to optimize product recommendation features serving over 200 Amazon Prime users worldwide and driving 20% revenue growth YoY.

Applicant 2:

Built machine learning development to help recommend products in an online marketplace.

Who would you want to talk to if you were a hiring manager? Applicant 1, who has clearly expressed the modes, extent, and outcome of their contributions, and whose contributions had an impact? Or Applicant 2, who didn’t provide any details about the size of the projects they worked on, the exact responsibilities they had, the user base they were serving, or the business outcomes? Ding ding ding: Applicant 1 gets the interview.

Customize your resume for each job you apply to.

In this article, we’ve been talking about building your machine learning resume, but in reality, you’re going to be building A LOT of resumes. While you might design a base resume to put on LinkedIn or to use while networking, for each job you apply for you should tailor this base resume to reflect the job description. Remember, your goal is to show the company that you would be a good fit, so it’s important to reframe your experience to match what they’re looking for as closely as possible. Just make sure to keep track of which version you are sending in case an interviewer refers to it in an interview.

Optimize your resume for applicant tracking systems.

As part of customizing your resume for a job description, you also want to optimize it for a company’s ATS. You can find lots of information online for how to do this for big companies, but in general, you want to make sure that your resume includes key technical skills that a job description mentions verbatim, as well as any software mentioned and other important keywords that pop out to you. Since you don’t have access to the ML algorithm that will crawl your resume, this ends up being more of an art than a science: you can’t be exactly sure what the ATS is looking for, but you want to try to provide enough for it to grab onto so that a human will read over your resume and, hopefully, want to talk to you.

Sample Machine Learning Resumes

We’ve just given you a bunch of information on the purpose and contents of a machine learning resume, as well as some tips to make sure that yours stands out from the pile — but sometimes it’s easier to start from an example rather than just starting from scratch. For that reason, below we’ve included examples of a machine learning engineer resume to give you an idea as you start drafting.

Once you’ve finalized your resume and started sending it out, head over to our article on interviewing to prepare yourself for the next step in the hiring process!