Acceptance rates for jobs at the FAANG companies tend to be under 1%. As you can imagine, most candidates don’t get past the resume screening.
To increase your chances of getting to the interview stage for a machine learning (ML) engineer role, use our step-by-step guide to writing a top ML engineer resume/CV.
Get tips and expert insights from tech recruiters and ML engineer coaches including REAL EXAMPLES of machine learning engineer resumes that earned candidates offers or interviews at FAANG (or "MAANG") companies such as Google, Amazon, and Apple.
Here’s an overview of what we’ll cover:
1. 7 golden rules for machine learning engineer resumes (from FAANG recruiters)
2. 7 key skills for your machine learning engineer resume
3. 6 REAL examples of machine learning engineer resumes that worked for Google, Apple, Amazon, etc.
4. Machine learning engineer resume template
5. How to write a machine learning engineer resume that gets you into FAANG (section-by-section)
Let’s get into it.
1. 7 golden rules for machine learning engineer resumes (from FAANG recruiters and coaches)↑
We asked Cody (top tech recruiter, ex-Google now at LinkedIn), Candace (career and resume expert), and Rishabh (Google senior machine learning engineer) and Yahia (ex-Meta senior research data scientist/ML engineer) what advice they'd give to someone writing a resume to get into FAANG/MAANG or similar.
They've helped a LOT of people get into FAANG and they evaluate tech resumes every day, so they know what they're talking about. This is what they came up with:
Tip #1 Answer recruiters' questions immediately
There is one thing that all recruiters and hiring managers want to know immediately: years of role-relevant experience.
And if you're applying for a management role, they'll also want to see how many years of management experience you have.
So, make it easy for them.
"Us recruiters are lazy. Don't make us dig around for the key info, we want to see if you meet the job requirements in the first 10 seconds!" says Cody.
To do this, you could include some bullet points with this key information at the top of your resume. This leads us to the next point...
Tip #2 Consider a non-traditional structure
The layout we recommend in section 4 is the traditional one, perhaps the safest. But it's not obligatory.
Cody prefers using two bullet-point summaries at the top of your resume to pack in the key information and your most impressive career achievements.
"The top of the resume is the prime real estate. Put the shiny bits, your best achievements, up top. That way you've got a better chance of grabbing the recruiter's attention."
This is what it looks like on page 1 of his resume:
Click to watch Cody's full explanation of this non-traditional resume format.
Tip #3 Avoid using design features
Unless you’re a product designer, and so there really is no upside to using a fancy resume design. It won't impress recruiters and, in a worst-case scenario, could actually prevent your resume from being properly processed.
"Design features like pictures, columns, photos, etc. can prevent ATS (applicant tracking systems) from correctly scanning your resume," says Candace.
You should also avoid including your photo in your resume, as this goes against employment and discrimination laws in most countries, and is another potential problem for ATS.
Tip #4 Be explicit about the locations you’re open to working at / remote
So many people fail to do this. But if FAANG recruiters are going to approach you for roles, rather than the other way around, they'll need to know the locations you're available to work at.
If you're willing to relocate for the right role, make that clear in your resume. So, instead of putting "San Francisco" under your name next to your email, maybe you put "Locations: San Francisco| Remote| Hybrid within a 30-mile radius of Bay Area".
Tip #5 Cut out all waffle
Recruiters like Cody and Candace see so many personal statements or 'objective' sections at the top of resumes which take up valuable space without saying much at all.
"Everything on your resume needs to be specific,” says Cody. "Putting ‘Experienced engineer passionate about making great products’ doesn't tell me anything. It wastes both space and seconds of the recruiter’s time. You've got to be more specific. How many years of experience? What great products have you made?"
When writing your work experience section, don’t just list your technical tasks, i.e., the models you deployed. An effective way to describe your ML experience, Yahia says, is to highlight the problem you solved using machine learning (e.g., “built an NLP-based document management suite to help users retrieve semantically relevant documents”)
Tip #6 Numbers tell a better story
This is worth repeating again and again: quantify your achievements. All the most effective resumes are packed full of metrics and numbers that put achievements in context.
“Clearly demonstrate the value you’ve added to the organization. For example, include metrics like cost savings, revenue generated, efficiency improvements, or time saved. These numbers showcase the tangible impact of your work,” Rishabh says.
Tip #7 Use a skills section to include keywords for your role
You don't want to jam your resume full of keywords, but with ATS increasingly used, it is important to make sure your resume mentions the necessary skills, tools and technologies. Candace says that a skills section can be a great place to list all these very efficiently.
"A "Skills" section can help recruiters quickly see if you fit their requirements, and is also a great way to get keywords into your resume. You could also consider a ‘technology snapshot’ type section if the jobs you’re applying for require experience in specific technologies."
2. 7 key skills for your machine learning engineer resume↑
The exact skills that you'll need to focus on in your resume will obviously depend on your role and the job description. But there are some key skills that are important in your ML engineer resume, with insights from Thang (ex-Meta/Apple engineer) Rishabh (Google senior ML engineer), and Yu-Sheng (Waymo ML tooling software engineer).
2.1 Leadership skills
Even if you're not applying for a leadership role, recruiters and hiring managers want to see that you have what it takes to become a leader if you're not one already.
Rishabh says, “Include specific examples of how you’ve solved complex problems or made significant contributions to projects. Additionally, emphasize any mentorship or leadership roles you’ve held, as these indicate your ability to collaborate and guide others effectively.”
If you haven’t got many strong examples from your work experience, try to find examples from personal projects or university (if recently graduated).
2.2 Communication skills
This is desirable even for very technical roles. Try to include your experience of working with cross-functional teams. If you're applying to a management role, demonstrate that you have experience aligning different stakeholders.
2.3 Data analysis skills
Data analysis is a crucial skill for machine learning engineers so you’ll need to demonstrate how you use data in your day-to-day work as an ML engineer, not just in training models but also in driving strategic decisions, responsible AI deployment, and more.
2.4 Facilitation skills
If you're applying for a management role, you need to show that you'll be able to help your team progress, remove obstacles, and solve blockages. It’s not always easy to get into this kind of detail on a resume, but try to include an example that shows how you unblocked a project, took preemptive action to avoid a bottleneck, or improved a process.
2.5 System design skills
System design skills are usually needed for ML engineering roles at FAANG. You'll need to be able to discuss engineering architecture and make decisions from a machine learning design perspective. So if you have any experience in designing systems, or related experience, make sure it’s prominent on your resume.
2.6 End-to-end machine learning experience
The most important thing you need to demonstrate in detail in your resume is your end-to-end experience in machine learning, according to Rishabh.
“Highlight your expertise across the entire ML lifecycle, from business understanding to data collection, processing, feature engineering, model training, validation, deployment, and monitoring. Recruiters look for candidates who understand ML holistically.”
In addition, when showcasing your ML projects in your resume, Yu says, “Try your best to mention the relevant tech stack in detail. This doesn’t just showcase your technical depth but also increases the chance of passing automated resume screens.”
When it comes to your experience with machine learning libraries, Thang’s advice is not to limit yourself to just one library if you’ve never used other Python ML libraries.
“For example, if you only used Tensorflow consider adding Pytorch and other ML libraries to your resume. Take a day or two to learn about how to use those libraries then add them to your resume after. A short amount of time learning these more ML libraries will increase your odds of landing more interviews,” he explains.
If you’re transitioning from a non-ML role but have worked with ML in your past roles, be sure to emphasize it. “For example, I'm a data engineer who builds data pipelines, I can emphasize working with ML by saying my pipelines feed into ML models as training data,” Thang says.
If you have none, consider working on an ML project first so that you can demonstrate that you can do the work.
2.7 New generative AI tech knowledge
According to Thang, you’ll find that a lot of roles right now are looking for new generative AI tech in your stack. This includes Large Language Model (LLM), Deep Learning, etc.
If you have no professional experience in such tech, you should prioritize working on an ML project integrating LLM or Deep Learning that you can showcase in your resume.
Right, let's see some example resumes.
3. 6 REAL machine learning engineer resume examples that worked for Google, Apple, etc.↑
Before we start guiding you on how to write your machine learning engineer resume step-by-step, take a look at some real examples that got their owners interviews at FAANG and other top companies.
You'll notice they follow different formats, and none fully follow the guidelines we set out below in section 5. We think this shows two things:
- there are many acceptable ways to write a resume
- your resume doesn't have to be perfect, as long as it demonstrates your skills and achievements effectively.
Note that we’ve anonymized the following resumes and used pseudonyms to protect our sources’ privacy.
Let's take a look.
3.1 Google senior machine learning engineer resume example
This is the resume that got “Rahul” a senior machine learning engineer role at Google.
Here are our thoughts on Rahul’s machine learning engineer resume:
- Quantified impact and emphasis on achievements. Rahul is not shy about detailing the big impact and results he’s achieved in each role. He makes sure to add success metrics to back them up. He also has a separate section summarizing his awards and other career highlights.
- Action verbs. Each bullet point, where relevant, starts with a powerful action verb.
3.2 Apple senior staff AI/machine learning engineer resume example
Let’s take a look at the extensive resume that got “Mark” a senior staff MLE role at Apple.
Here are our thoughts on Mark’s MLE resume:
- Quite lengthy and detailed. Mark’s resume could use a little more streamlining (it’s 3 1/2 pages long). However, his extensive professional experience relevant to ML warrants most of the length. He makes his career highlights easy to digest at a glance by summarizing them at the top.
- Emphasis on ML experience. He makes sure to highlight how he used ML to solve problems in his other roles which may not be ML-focused.
3.3 Senior machine learning engineer resume example
“Yeshua” currently works for a top social media company as a senior machine learning engineer, and this is the resume that got him that role.
Here’s our feedback on his resume:
- Highlight leadership and cross-functional experience. Yeshua highlights his leadership and collaborative experience in each role he’s had.
- Extensive ML experience. In his bullet points, he emphasizes how he used machine learning to solve problems and optimize features, quantifying results (“15% reduction in user-reported spam incidents”) where relevant.
3.4 Google software engineer - machine learning resume example
Here are some of our observations:
- Relevant background: Ravi’s background in data science and machine learning makes him a particularly attractive candidate for a Google MLE role.
- Concise: He summarizes his technologies and techniques at the very beginning of his resume and offers quick descriptions of his previous roles, highlighting the ML technologies he has used to solve problems.
3.5 Google/Waymo machine learning software engineer resume example
This is the resume that helped “Kevin” land his software engineer role at Google and then eventually his ML tooling software engineer position at Waymo (formerly the Google Self-Driving Car Project).
Here are our thoughts on “Kevin’s” resume:
- Detailed ML projects: He makes sure to describe in detail the ML projects he worked on complete with the tech stack and quantified impact.
- Extracurricular activities and achievements: Apart from his advanced degree, he includes his academic and volunteer experiences as well as his four ML-related published articles. Including them may have made the resume a bit long, but as the achievements he included are related to ML and engineering, they show his passion for the subject.
3.6 Amazon senior software development engineer - machine learning resume example
“Vikash” is currently an adjunct lecturer and a senior machine learning software engineer at Amazon Ads, and here’s his current resume.
4. Machine learning engineer resume template↑
Unlike the examples listed above, this is not a real MLE resume. Instead, it's the resume of an imaginary mid-level machine learning engineer called Jessica. It’s an amalgamation of the many high-quality resumes that candidates have shared with us before going on to work at Google, Meta, Amazon, etc.
Click here to get this MLE resume template as a Google doc.
Right, let’s take the first step in building a machine learning engineer resume that's good enough to get into FAANG.
5. How to write a machine learning engineer resume that gets you into FAANG (section-by-section)↑
Now that you’ve seen examples of what you should be aiming for, as well as some key tips, let’s go through the resume-building process, step-by-step.
5.1 Study the target company and job descriptions
Before you start writing or editing your ML engineer resume, our tip is that you do some research.
Find a job specification for the sort of FAANG role you’re targeting, read it thoroughly, and use it to shape your resume in the following ways:
- First of all, work out what type of ML engineer profile the job description is looking for. Which skills will be most crucial for the role? Prepare to adapt your resume’s content accordingly.
- Zoom in on a few of the responsibilities in the job description that you think are most important. Search for specific examples from your past that demonstrate experience in doing the same thing or something very similar. Find the numbers to back it up where possible, so you’re ready to include this information in the work experience section later on.
- Take note of the language used in the job description so you can, where appropriate, match specific verbs and phrases.
- Research the company. For example, imagine you’re targeting a machine learning engineer role at Meta. Meta has 6 core values, so you’d want to make sure that your resume transmits these values too, or at least doesn’t contradict them. That could mean including a volunteering activity under Interests to show that you like to "build social value." Follow the same logic if you’re applying for Google or Amazon.
Does all this mean you’ll need a different iteration of your resume for every company or ML engineer role you target?
Ideally yes, but there will be a lot of overlap, so usually you’ll only need to make a few strategic edits.
5.2 Choose a design
The design of your resume should have one objective: to convey as much information as possible in a way that is clear, easy to digest, and professional. Use our resume template as your template, and you’ve already achieved that!
Some people add a second objective: to demonstrate strong design skills to stand out from the crowd and impress the recruiter.
However, some recruiters might even be put off by a “creative” or unique design and, in a worst-case scenario, it could prevent your resume from being properly processed.
Keep it clean and simple. You should also avoid including your photo in your resume, as this goes against employment and discrimination laws in most countries, and is another potential problem for the ATS.
5.3 Choose your sections
There are lots of ways to write a machine learning engineer resume and the exact sections you include are up to you. We recommend using the following sections for a machine learning engineer resume because we know this approach works for companies such as Google, Meta, and Amazon, for both junior and experienced candidates.
- Personal information
- Work experience
- Education
- Skills/Tools
- Interests/Extracurricular
You may want to tweak the order. For example, if you’ve just graduated or have just a year or two of experience, Google recommends starting with your education section.
5.4 Start writing!
The good news is, you don’t have to get it perfect the first time. A strong resume is usually one that is re-written and tweaked multiple times.
We’ve spoken to tech recruiters to get guidance on how to write each section. Let’s take a look.
5.4.1 Personal information section
This section is not the place to try and impress. Just make sure you get your key details across as concisely as possible.
DO:
- Use a bigger font for your name than for the rest of the section to make it stand out
- Include your name, email address, phone number, city/county you live in
- Ideally, include a link to your LinkedIn profile (or Github if you have an engineering background)
DON’T:
- Title this section. It’s not necessary in this type of layout, so save the space
- Include a street address, it’s unnecessary and unsafe
- Include a photo, date of birth, or gender, unless specifically requested to do so
- Label each piece of information e.g. “email:”, “tel:”, etc. It’s obvious what they are, so save the space
5.4.2 Work experience section
This is probably the most important part of your resume to get right, and the easiest to get wrong. Many candidates think that their work experience speaks for itself, and simply list their role and a few of their main responsibilities.
However, we recommend a much more powerful approach.
Instead of listing responsibilities, you need to talk about actions. This means starting each bullet point with an action verb. These verbs should relate to the key skills from section 2 that companies look for in ML engineer resumes (Leadership, Communication, Facilitation, Data analysis, etc). "Executed," "Unblocked," "Led," and "Delivered" are some good examples of such verbs.
Choosing actions that are relevant to the essential machine learning skills will also mean that your resume contains the keywords that recruiters (and sometimes Applicant Tracking Systems) will be looking for.
You should also focus on the results of what you did and quantify them as much as possible to highlight the tangible contributions you have made. Ex-Google SVP Lazlo Bock talks about a common method for doing this that you might find helpful, called the “X, Y, Z” formula:
“Accomplished [X] as measured by [Y] by doing [Z]”.
For example, “Decreased server response time by 30% by implementing machine learning implementation”.
DO:
- Use reverse chronological order, putting most recent employment at the top
- Use present tense verbs (e.g. "Lead, Coordinate, Execute") in your current position (except for completed achievements), and past tense verbs for past positions and completed achievements (e.g. "Led, Coordinated, Executed")
- Include the programming language and ML libraries you have experience using
- Describe your actions and what they achieved
- Include metrics to quantify what your actions achieved where possible
- Study the language of the job description and where appropriate, match it
- Make sure you’ve naturally included several relevant keywords
- Demonstrate a balance of skills
DON’T:
- Be shy and humble. Now is not the time!
- Just put your responsibilities
- Be vague
- Go so overboard with numbers that it looks like a math problem. It still needs to be easy to read
- Include lots of buzzwords just for the sake of it
5.4.3 Education section
This section should be extremely concise and clear. Hopefully, your educational achievements can do the talking for you, as all you can really do here is present the necessary information with the right level of detail.
Note that if you have recently graduated and only have internship experiences instead of relevant work experience, this section should follow the Personal Information section, and you may want to go into a bit more detail. Otherwise, you can include it after work experience.
Follow the tips below to make sure you get it just right.
DO:
- If you have multiple degrees (e.g. a BA and an MBA), you should write a subsection for each degree, starting with your highest level of education first (e.g. your Masters degree)
- For each degree, include the name of the degree, university, and dates in the headline. If you’re a recent graduate, you can also list any subjects you have taken that are relevant to your role (data modeling and analysis, machine learning algorithms, etc.)
- List your grades (e.g. GPA) as well as results of other standardized tests you have taken (e.g. SAT, GMAT, etc.) that demonstrate your intellect
- Detail any awards and scholarships you received at university level and most importantly how competitive they were (e.g. two awards for 1,000 students)
- If you don’t have much ML work experience, you might want to include tech bootcamps, a link to your projects, or online courses you’ve taken (e.g. Udacity)
DON’T:
- Panic if you don't have a degree. You don’t have to have gone to college to get into a FAANG company. Instead, put your high school grades and any relevant educational qualifications you gained after school
- Include high school experience if you've already graduated
- Include your thesis / dissertation unless you're a fairly recent graduate, in which case you should summarize the topic in a way that's VERY easy to understand
5.4.4 Awards and Leadership section
We've labelled this section "Awards & Leadership" instead of "Extracurricular" section for two reasons:
1. Google uses it as its recommended resume template (see here)
2. Extracurricular activities are less important for technical roles like machine learning engineer.
The more experience you have, the easier it should be for you to find two or three strong bullet points that demonstrate leadership (outside your day-to-day work) or awards.
If you haven't won any awards or can't think of any strong leadership examples outside your day-to-day role, then consider leaving this section entirely.
DO:
- Put awards in context. E.g. "1st out of 22 applicants".
- Consider leaving this section out if you're lacking content.
DON'T:
- Use awards from school or university if you graduated more than ten years ago
- Include weaker achievements (e.g "employee of the week") just to fill the space
5.4.5 Additional Skills & Interests section
An ML engineer resume needs to show that you're adept at using a wide range of ML tools, libraries, and technologies. Listing them here can make it easy for a recruiter to quickly check you meet their requirements.
DO:
- If you need to save vertical space, list skills in sentences rather than bullets
DON’T:
- Include generic, uninteresting things that everyone likes doing, like “watching Netflix” or “hanging out with friends,” as interests
- List basic skills that almost everyone has, such as "Google Docs" or "MS Word".
5.5 Proofreading and feedback
Don’t skip this step! Use a grammar-checking tool and then proofread until it’s perfect. This is harder than it sounds because multiple reviews and tweaking after the initial proofread can easily create new hard-to-spot errors. The only solution is to proofread again after each tweak.
We recommend saving your resume as a PDF file unless the job description says otherwise, and checking it opens properly (with the correct formatting) on a Mac or PC.
Receiving feedback is also important. Share it with a friend or partner, and they’ll be very likely to see mistakes that you haven’t noticed. Of course, if you can share it with an experienced tech recruiter / interviewer, that can give you a big advantage over other applicants.
DO:
- Proofread from top to bottom and then read it in reverse to check spelling
- If you’ve tweaked it, proofread again before sending
- Check the file opens properly on Mac and PC
- Get feedback on it before sending
DON’T:
- Send it with typos. Your resume is your first impression, make it count
6. Your machine learning engineer resume checklist↑
Almost ready to send your machine learning engineer resume? Use this checklist to make sure you’re following the best practices we’ve recommended above.
If you can answer “Yes” to every question, then you’re ready to hit "Apply" or upload it to a popular technical jobs site.
General
- Does your resume present you as the type of machine learning engineer candidate the job description is looking for?
Layout
- Is it just one page? If not, do you have the experience to merit 2 or more pages?
- Is the formatting 100% consistent and neat?
- Is there enough white space to breathe?
Personal Information
- Have you checked your contact details are correct?
Work Experience
- Have you talked about your actions rather than your responsibilities?
- Have you quantified the impact of your actions?
- Have you demonstrated a range of relevant skills? Have you listed your programming languages and ML libraries? Have you included new generative AI tech in your stack?
Awards & Leadership
- If you graduated >10 years ago, are your examples post-university?
Skills & Interests
- Have you listed all the ML-relevant knowledge you’re familiar with?
- Do your interests make you stand out from the crowd in some way?
Proofreading and feedback
- Have you proofread it since you last edited it?
- Have you received any feedback on your resume and updated it?
- Have you saved it as a PDF to make sure it displays correctly on all devices?
Did you say “Yes” to every question? Well done! If you’ve used all the tips in this article, then your resume should be in good condition and will give you a fighting chance of getting a position at a big tech company.
7. Is your ML engineer resume good enough for FAANG?↑
If you're going for one of the top tech jobs, having a machine learning engineer resume that's "fine" may not be enough. To get your ML engineer resume from "fine" to "outstanding" usually requires feedback from someone who really knows their stuff - as in an ex-recruiter or manager at one of the top companies.
We know it's hard to get access to those types of people. That's why we've created a resume review service, that allows you to get immediate feedback on your resume with a top recruiter/coach of your choosing. Take a look!
