Machine Learning Engineer Resume: Guide, Free Templates, & Examples

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You’re used to designing complex artificial intelligence systems as a Machine Learning Engineer. However, is programming your resume correctly a challenge? If that’s the case, use our tailored resume templates to design the perfect job-winning algorithm for any vacancy.

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Have you ever dreamt of creating an algorithm that’s powerful enough to craft you the perfect machine learning resume?

Well, we’ve got both good news and bad news. 

The bad news is that submitting a resume that lacks your personal human touch is unlikely to get you good results. 

The good news is that if you’ve ever dreamt about machine learning solutions, you’ve probably already got the necessary passion to succeed in this growing field.  

However, having a love for technology won’t be enough to get you hired. In fact, due to the high demand for these types of positions, many employers require a Ph.D. or MSc.

But don’t worry, you’re in luck. We’re here to help you write or update your resume to get you noticed.

Even without one of the aforementioned qualifications, if you have any related work experience or projects to add to your resume, you’ll have a high chance of getting hired.

A machine-learning skill set will often get you further than a piece of paper stating that you are an expert in the subject area. 

Whether you’re competent in programming, data modeling, algorithms, or any other relevant field, there’s a breadth of opportunities available in this blossoming industry. 

We’re also going to show you the essential steps from picking the correct format to what keywords to include that will put you on the path to landing the job of your dreams.

Are you interested in seeing more resources related to the machine learning and AI sector? If so, be sure to check out our AI Developer resume too.

Try to think of your life as a disorganized set of data. 

You need to collect, clean, and consolidate the important data points. Create a way to visualize them and present the most relevant insights. 

To put it into more concrete terms, you need to use a resume format that enables you to include all the essential information that will get you hired. 

Essentially you need to present your resume dataset for machine learning in the best way possible. 

This is critical when deciding which structure to use.

If you have to choose one type of format, a combination format is the best one to properly show off a Machine Learning Engineer profile because it effectively displays both your technical skills and relevant work history simultaneously. 

This hybrid format offers several advantages over the traditional reverse-chronological or functional formats because it can show off your:

1. Technical proficiency: The combination format showcases your expertise in programming languages, frameworks, and tools (e.g., sci-kit-learn, pandas), allowing recruiters to quickly gauge your technical capabilities.
2. Relevant experience: Combination formats can also highlight your involvement in projects such as developing recommendation systems, creating fraud detection models, or implementing natural language processing algorithms, demonstrating your practical experience in the field.
3. Problem-solving abilities: By combining your skills, such as feature engineering and model optimization, with your experience in addressing real-world challenges, such as reducing latency, you can outline your problem-solving capabilities.
4. Collaboration and communication: The format emphasizes your ability to work with data scientists, software engineers, and product managers, as well as communicate technical concepts to non-technical stakeholders, illustrating your interpersonal skills
5. Continuous learning: You can easily explain your commitment to staying updated by highlighting your participation in courses, workshops, or certifications, for example, if you’ve attended a conference to learn about new techniques, models, and advancements in machine learning.

Some applicants try out other formats, such as functional resumes, which put their skills over their experience. 

For example, if you’re interested in a position, and you lack experience for that specific role, it might make sense to try using this approach to focus on your skills.

By using our guided resume builder, you can also have an easier time showing off both your skills and experience correctly.

Now that we’ve covered what kind of formatting to use, let’s take a gander at what an actual resume should look like to get a better understanding of how it should look.

This will help ensure that the document you create will light up the neural networks of both the hiring managers and the C-suite at your dream company. 

The following example includes a machine learning resume for 2 years of experience, which was created according to all the fundamentals and winning strategies we’ll cover in this article: 

Resume Example

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[Luke Holloway]

[Machine Learning Engineer]

[ Los Angeles, CA 90004-2535 | 555-555-5555 | lholloway@randomemail.com]

Summary

Industrious Machine Learning Engineer with 2+ years of experience building successful algorithms and predictive models at MobiDev. Highly adept at K-Means and Mean-Shift clustering. Motivated engineer and analyst proficient with various programming languages and the ability to apply ML techniques to solve business problems. Developed an algorithm to predict product sales within 2%.

Experience

Machine Learning Engineer

MobiDev 2019 – 2020

• Employed programming languages such as Python and C++ to write software prototypes. Analyzed 50+ complex simulation datasets with logistic regression models.
• Researched the software market for solutions to client needs.
• Predicted product sales to an accuracy of 2% through predictive analytics algorithms. Translated business problems into deep learning models to produce results from data inputs.
• Improved simulation accuracy by 15% through ML algorithms.

Education

PhD in Machine Learning

Carnegie Mellon

2017 – 2020

Research paper Deep Neural Decision-Making published in Journal of Machine Learning Research.

Senior predictive modeling project posted on LifeHacker.

MSc in Machine Learning

Cornell University

2017 – 2020

Achieved a 3.6 GPA.

Relevant coursework: deep reinforcement learning, convex optimization, probability & mathematical statistics, algorithms for NLP, computer vision.

Clubs and societies: AI Society, TedX Club.

BSc in Information Technology

Cornell University

2017 – 2020

Achieved a 3.92 GPA (Top 1% of the program).

Skills

• Machine learning algorithms
• Natural language processing
• Clustering models C++, Java, and Python
• Regression Tensorflow
• Data modeling and exploration
• Critical thinking
• Research

Certifications

IBM Machine Learning Professional Certificate

Cloudera Data Platform Generalist Certification

Publications

Abstract Reasoning in Neural Networks published in Neurocomputing

Looking like a world-beater right? 

In this example, you can see experience and skills that are highly tailored to the job, and figures to back up everything that’s being said.

Remember to take advantage of a machine learning project template as well, to give yourself a helping hand in creating a resume like this one.

Now let’s take a look at each section one by one in order to provide you with the right reinforcement learning that will help you make the correct resume-building decisions.

Before jumping into the sections that are a bit more elaborate, you’ll want to write a brief resume headline to quickly introduce yourself.

If you do it right, it sets the correct tone for the rest of your resume. 

After the title of your resume, which should be Machine Learning Engineer, you can add your headline, which is a short sentence that explains your:

• Qualifications
• Work history
• Job title

It’s a quick sentence, but remember, be specific! 

Here is an example of how you should create your resume headline.

Machine Learning Engineer specializing in creating predictive analytics algorithms

Just like well-written code it looks efficient and it works! 

You can also add the number of years you’ve been working as an engineer or focused on your specialty. 

If we want to shine bright in what sometimes feels like the far, far away galaxy of career success, a resume summary or objective is essential.  

Have you ever been scrolling on a particular tech forum looking for an answer to a complex problem, only to find that the solution is a few thousand words too long? 

Well, that’s where TL;DR comes in. This popular internet acronym stands for “Too Long; Didn’t read”, and it’s exactly how you should view this part of your resume.

Recruiters look at hundreds, maybe even thousands of resumes every day, often with only a few seconds to look at each one. 

That means that even the most perfectly crafted resumes will only be graced with moments of attention.

Does that discourage you from creating the perfect resume?

Well, don’t be afraid. 

Your resume summary or objective is exactly the section that you can use to combat this issue by quickly grabbing a recruiter’s attention. 

Here is some advice you should follow to write an objective or summary of a machine learning resume:

1. Begin with a power adjective such as expert or results-oriented
2. Include your job title of Machine Learning Engineer or machine learning enthusiast
3. Share your amount of experience in years (1, 3, 10+) 
4. Add your most relevant achievements, responsibilities, and key skills 
5. Back up your claims with data and real figures
6. Use action verbs to describe your past positions
7. Explain how you will add value to the company — for example, bringing a business-minded approach to a specific project at a particular company

If you don’t have much experience yet, don’t worry, just make sure all of your programming skills shine. 

Machine Learning Engineer Resume Summary Example

While machine learning is comparable to rocket science, writing a summary for your resume definitely isn’t. 

However, it’s important that your machine learning synopsis is both engaging and unique.

This is key as the main focus of this section is to get the attention of hiring managers or recruiters and to get them to keep reading. 

You’ll want to focus on your technical skills and knowledge while also showing off your experience.

Let’s take a look at a bad example first, and then let’s see if we can “human learn” our way into making a better one. 

So…could a machine have written that? 

The answer is, probably yes. And while that might be impressive to some degree, our goal is not to spam recruiters with robotic-sounding text. 

Our summary should be infused with bespoke content that speaks both to our true strengths and the benefits we will bring to our prospective employer. 

Here is a much more effective example: 

That one definitely passes the Turing test, don’t you think? 

This summary is much more effective at portraying the unique value of the candidate, while specifically targeting the company and job being applied to. 

After reading this piece, it’s clear what key skills and past achievements the applicant will bring to the position. 

So let’s make sure to keep this example when formulating your personal resume-creating mental algorithm. 

How to Write Entry-Level Machine Learning Resume Objective

What if you’re creating a junior Machine Learning Engineer resume Are you destined for a bland intro that looks like it was created by a spambot? 

You can’t avoid the fact that you’re inexperienced, unfortunately.

The best solution is to simply use a resume objective instead. 

When you write your resume objective you should include information on your industry knowledge, education, any related experience even if is not ‘actual’ work experience, and your motivation. This outlines what you can offer the company.

Here you’ll want to mention the type of job or industry you want to work in or even the skills you want to build. 

Just make sure that these match the job description, and if possible include any relevant keywords from the job listing as well.

Luckily there’s a breadth of ​​entry-level Machine Learning Engineer jobs due to the industry’s skyrocketing growth. 

You can use the following machine learning resume keywords to include in your objective to help demonstrate your capability in different areas of machine learning.

 Core Machine Learning Concepts

• Supervised learning
• Unsupervised learning
• Model validation
• Ensemble methods
• Overfitting

Tools and Libraries

• Python
• R
• TensorFlow
• LightGBM
• XGBoost

Evaluation Metrics

• Accuracy
• Precision
• Confusion Matrix
• Recall
• Mean squared Error

Ultimately the keywords you use will depend on what the manager is asking for in the job description, however, any of these will look impressive in your objective and help you get through the applicant tracking system as well.

Entry-Level Machine Learning Engineer Resume Objectives

Now that we’ve covered what exactly a resume objective is, let’s take a look at some winning examples. 

Let’s start with an over-the-top example, which is more of a humble (or not so humble) brag rather than a professional resume objective. 

This is how not to do it: 

This example says a lot about themselves, but it doesn’t display anything about their goals or what they want to achieve.

It’s also quite hyperbolic (which might even be an understatement). 

Let your past metrics show off for you. 

Don’t just brag, make your motivations and potential clear in an impressive but objective manner that keeps recruiters interested without pushing them away. 

Even as an entry-level applicant, you can make recruiters say “wow”. Think freelancing, personal projects, or even hobbies. 

Here’s a well-written example that does just this:

If that’s not a solution to the job-finding equation, then I don’t know what is. 

Overall, this resume objective is leaps and bounds above the other example. It makes use of relevant figures and effectively includes a captivating goal that matches the job description. 

It even mentions some key skills for Machine Learning Engineer positions, making up for the lacking experience.  

You’re an aficionado of the machine world. 

But what happens when you decide to enter the Matrix and throw yourself into the human world of career-building? 

You’ll need to know how to add machine learning to your resume through relevant work experience in a way that will make your professional profile stand out from other candidates.

Whether you’re looking for a job focused on designing machine learning systems, researching ML algorithms, or analyzing data, the way you describe your experience is extremely important. 

Especially if you don’t have a Ph.D., then your experience section is arguably the most significant part of your machine learning resume. 

Machine Learning Engineer Resume Examples: Experience

Let’s be honest. Your hiring manager doesn’t understand what you do. 

The executives pretend to know what you do.

And you’ve probably made a loved one’s head hurt more than once trying to talk to them about one of your projects at work.

But when you’re applying for a machine learning position, experience is often the principal deciding factor for hiring managers and HR professionals.

However, you’ll need to show that you’ve gotten results using models thanks to your skills when you mention your experience.

Take a look at an eye-catching example that will put you right on the path to becoming the next Geoff Hilton or Andrew Ng:

If that experience entry doesn’t look like quantum supremacy, then we don’t know what does. 

Not only is it concise and easy to read, but it includes relevant responsibilities and achievements to clearly show how the candidate is very well-qualified. 

It’s also perfectly tailored to the Machine Learning Engineering position or company they’re applying to.

Considering that most recruiters only devote a few seconds to reading each resume, it’s important that every sentence includes valuable information that promotes the applicant.

On the other hand, here is an example that might require some troubleshooting and debugging: 

This looks more like a Commodore 64 rather than a Fugaku supercomputer, and for good reason.  

Not only is there almost no information about the position and company the candidate was working at, but the experiences also fall flat.

This is because they lack the necessary achievements and figures to make them stand out. 

For example, when mentioning the use of common programming languages, they had a golden opportunity to use examples, such as Python, which is also the most popular language for machine learning, or C++. 

Instead, they stopped there, leaving little room for keywords to activate the neural networks of a recruiter. 

Entry-Level Machine Learning Engineer Resume: Experience Section

Did you once try to build your own Wall-E in your backyard? 

Have you ever created a sentiment analysis to beat your friends in fantasy football? 

You’ll be happy to know that all that work was not to waste. 

For a Machine Learning Engineer resume for freshers, in other words, entry-level professionals, you’ll need to gather your best programming or engineering achievements that show you have a Machine Learning Engineer skill set. 

Hype up any past software engineering projects and responsibilities or transferable skills that are related to machine learning.

You can even just include assignments or extra courses that show you have teamwork skills, analytical thinking, or an affinity for the handling of data.

You’ve spent countless hours smothered in complicated textbooks that look more like an AI’s secret diary than university material.  

While your friends were out and about, you may have found yourself wrangling data into the long hours of the night and desperately trying to create your own specialized algorithms. 

The time has come for that work to finally come to fruition. 

So let’s make sure not to waste it and to avoid formulating your education section in a way that feels generic, boring, or downright spammy.

Machine Learning Engineer Resume Education Section

When writing your education section, it’s not enough to just list the program and university you studied at. 

Especially for less-experienced engineers, your education says a lot about your capabilities, while simultaneously showing that you have the right knowledge base to build upon.

In general, listing a bachelor’s degree is basically indispensable, and an MSc is recommended too. 

If you even have a Ph.D. to include, then you might already be on the path to becoming the next Yann LeCun.

Either way, it’s not enough to just list your education. You need to show that you excelled. 

Include any research papers, notable coursework, or any student associations you joined while in college. 

Feel free to also add your GPA, but only if it’s high, otherwise we suggest leaving it out.

Here are two examples to go on, one with a high chance of leading to an interview, and another that will leave the candidate complaining in their favorite Discord channel.  

Let’s start with the negative to understand what algorithm you should avoid in your resume. 

This example of your education section would be similar to the common, but painful, feeling of a senior ML engineer changing all the code you wrote in a code review session. 

Why is that?

Although it’s great to show that you engaged in varied activities while studying, they need to make you seem like a good fit for the role. This clearly doesn’t and the section needs an upgrade.

Have a look at how your education section should be programmed.

This example will get you an “A” in the hiring process.

This example mentions extra information which will help the candidate get hired like being part of a student club that is related to machine learning, and it clearly displays relevant coursework.

You’ve probably spent a lot of time learning about highly sophisticated tools, and the shed, or rather, your favorite coding program, might even feel like home.

This means that we don’t need to be the ones to tell you that any excellent resume, should include an extensive list of skills and abilities related to the job.

But how do you select and prioritize the capabilities to add to your document? The hard skills and software you can use should be given priority, and you can list them directly below your resume summary or objective.

Try to weave your soft skills throughout your resume, demonstrating them in your work experience or education section.

Important soft skills for Machine Learning Engineers include problem-solving skills, analytical skills, the ability to learn and adapt quickly, and the ability to communicate complicated ideas in easy language.

As a Machine Learning Engineer, you might be less concerned with man’s impending war against the machines, and more with how to beat other candidates.

After all, the complexity of a deep artificial neural network might seem like a piece of cake in comparison to navigating the competitive machine learning job market. 

So how do you stand out from the ever-growing crowd of machine learning experts? 

The answer is other sections. These are bonus sections at the end of your resume that can help show your job-related expertise. 

Maybe you’ve completed a machine learning crash course with Google AI.

Perhaps you’ve obtained a machine learning TensorFlow specialization. 

Or you even made the winning algorithm for your university’s applied machine learning club. 

Any extra information of this kind can be extremely useful to paint a clearer picture (or program a better code) for HR professionals reading your resume.

In other words, it will save your document from looking like a shaky frontend interface, while making you seem like more of a full-stack human.

Machine Learning Engineer Resume Sample “Other” Sections

Is your machine learning resume looking a bit slim? 

If yes, one of the best ways to fill it up is through other sections.

Here you can show how any extra information may be helpful to a recruiter, such as:

• Professional associations 
• Publications 
• Awards, 
• Certifications, 
• Volunteer work 

For example, did you go to the International Conference on Machine Learning? Or the Annual Conference on Neural Information Processing systems?

Well, this is where you can finally mention it without it going over someone’s heads. 

Here you can also include any good machine learning projects you’ve undertaken, as well as hobbies and interests, which can help add more color to your professional and personal profile. 

However, when adding all this extra information, you need to make it specific and evident in terms of how it will bring value and apply to your future role. 

Remember that it’s fundamental to optimize space on your resume, and sharing your passion for creating deep fakes of famous politicians with your friends doesn’t cut it. 

Here are some well-made examples to follow, including relevant machine learning resume projects:

Starting to get the right idea?

All these entries add additional value to the candidate’s resume, either by showing transferable skills or pertinent experience. 

Here are some extra sections that may do you more harm than good when trying to emphasize machine learning on a resume: 

Remember, space is a premium on your resume. 

So while there may be fellow “Magic: The Gathering” players at your future office, that won’t help your application in comparison to putting Python on a machine learning resume.

All in all, the above examples don’t give recruiters any idea that the candidate could bring value to a machine learning role, so there’s not any point in including them.

You’ve learned about the perfect resume-building algorithm. Now it’s time to start creating your resume.

Here are the essential strategies to follow when designing the best machine learning resume:

• Use figures, data, and statistics to back up any past achievements you include.
• Add keywords from the machine learning resume skills you find in the listing of the position you’re applying for.
• Use helpful tools to avoid mistakes like our builder or customizable templates.
• Include any high-level education such as a Ph.D., MSc, or BSc, as well as any related academic or extracurricular accomplishments and continued learning certifications.
• Incorporate any personal projects or publications related to machine learning. 

Take out all the guesswork and get help through step-by-step instructions and expert guidance in every stage of the hiring process.