Machine Learning Engineer Resume: Templates, Examples & Essential Skills
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.
After all, if they were looking to hire a robot, they wouldn’t have even posted the job in the first place.
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 PhD or MSc.
But don’t worry, you’re in luck.
Even without one of those 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.
All you need is a machine learning resume template that you can personalize for whichever position you’re applying for. And that’s exactly what we’ll give you.
We’re also going to show you the essential steps to making a resume that will put you on the path to landing the job of your dreams.
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What’s the Best Machine Learning Engineer Resume Format?
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 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 especially important when deciding which structure to use.
Luckily, in almost every case, you can rely on the reverse-chronological format.
Why should I use that for my machine learning resume pdf?
That’s because it’s the one that is based on presenting the most crucial element which recruiters are looking for when reading your resume: your experience.
With this format, the most recent machine learning experience on your resume will be prioritized, and your skills and education will also stand out.
Some applicants try out other resume formats, such as functional resumes, which put their skills first instead.
But most hiring managers don’t expect this kind of formatting, and that can penalize you in the hiring process.
However, this is not always the case.
For example, if you’re creating a machine learning researcher resume, and you lack experience for that specific role, it might make sense to try using a different approach.
💡 Top Tip
Even an inexperienced candidate can fill a reverse-chronological resume through side projects, internships, and even boot camps or machine learning crash courses.
This way, you can emphasize how you have the necessary skills for a machine learning engineer position, even if you don’t yet have the necessary experience.
That being said, as long as you have some details to show in your experience and education sections, a reverse-chronological layout should still bring you better results.
Machine Learning Sample Resume
Now that we’ve covered what kind of formatting to use, let’s take a gander at an actual resume of a machine learning engineer 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 experience, which was created according to all the fundamentals and winning strategies we’ll cover in this article:
[Luke Holloway] [Machine Learning Engineer] [4934 Maplewood, Los Angeles, CA 90004-2535 | 213-555-0198 | email@example.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 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 machine learning engineer resume example you can see experience and skills that are highly tailored to the job, and figures to back up everything that’s being said.
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.
How to Write a Machine Learning Engineer Resume Summary or Objective
If we want to shine bright in what sometimes feels like the far, far away galaxy of career success, a resume summary 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.
And no, you should try to copy a machine learning engineer resume from GitHub profiles.
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 a few seconds of attention.
Does that discourage you from creating the perfect machine learning developer resume?
Well, don’t be.
Your resume summary is exactly the section that you can use to combat this issue.
💡 Top Tip
This section is your highlight reel. Use it to summarize your accomplishments, sell the best features of your profile, and show why you’re the right fit for the job.
Here are the key steps you should take to write a summary of a machine learning resume:
- Begin with a power adjective such as expert or results-oriented
- Include your job title of machine learning engineer or machine learning enthusiast
- Share your amount of experience in years (1, 3, 10+)
- Add your most relevant achievements, responsibilities, and key skills
- Back up your claims with data and real figures
- Use action verbs to describe your past experiences
- Explain how you will add value to the company — for example, bringing a business-minded approach to a specific project at a particular company
Machine Learning Engineer Resume Summary Example
While machine learning is comparable to rocket science, writing a summary on a machine learning 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.
To ensure that you manage this, you’ll need to personalize your summary or objective to the company and position you’re applying for.
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.
Hard-working machine learning engineer experienced in data modeling and developing machine learning algorithms. Skilled individual with extensive knowledge of Python and predictive analytics.
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:
Versatile machine learning engineer with 5+ years of experience developing successful predictive models and algorithms for different industries. At Microsoft, applied data analysis and visualization to reduce project costs by 18%.
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
Earlier, we learned how to prototype the perfect resume summary when looking at a verified machine learning resume sample.
But what if we don’t have much experience to note? Are we destined for a bland intro that looks like it was created by a spambot?
One way to resolve this conundrum is to center your summary on your education or machine learning projects in your resume.
Another potentially easier method to skirt this issue is to simply use a resume objective instead.
While a resume summary conveys your qualifications and why you’re a good fit for the role, an objective is a short statement that focuses on your career goals.
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.
💡 Top Tip
Add keywords from the job listing in your resume objective to score highly on any applicant tracking systems (ATS) your prospective employer might be using.
Luckily there’s a breadth of entry-level machine learning engineer jobs due to the industry’s skyrocketing growth.
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:
Highly skilled individual with knowledge in machine learning. Entry-level machine learning engineer that works incredibly well in groups and also individually. Has some experience with designing machine learning models and visualizing data.
This example says a whole 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:
Passionate MSc student at Carnegie Mellon University, made the dean’s list for two consecutive years (2019-2020). Eager to help design machine learning models for Salesforce. Developed personal data modeling project which reduced business costs for a local business by 15%.
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 of the key skills for machine learning engineer positions, making up for the lacking experience.
How to Describe Your Machine Learning Engineer 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 in your resume through relevant experiences 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 PhD, then your experience section is the most important part of your machine learning resume.
Here are some expert tips to follow when writing it:
- Tailor your descriptions to the company and role you’re applying for
- Start with the job title
- Include the company name
- Insert the years/months you worked in the position
- Write 3-6 bullet points mentioning responsibilities and achievements during the role
- Back up your claims with figures and statistics
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 the principal deciding factor for hiring managers and HR professionals.
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:
Machine Learning Engineer
October 2018 to March 2021
- Built predictive models to track users preemptively.
- Developed machine learning models that detect anomalies with 95% accuracy and 0.1% false positives.
- Interpreted 120 new datasets to enhance existing models and decision-making.
- Reduced delivery costs by $800K through the application of data mining to a shipping issue.
- Designed machine learning algorithm that evaluated to 87%.
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 which promotes the applicant.
On the other hand, here is an example that might require some troubleshooting and debugging:
- Leveraged data for machine learning models
- Built algorithms to reduce manufacturing costs
- Employed common programming languages
- Edited technical documentation
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 or C++.
Instead, they stopped there, leaving little room for keywords to activate the neural networks of a recruiter.
Even more importantly, most companies today have implemented applicant tracking systems (ATS) to automatically review thousands of resumes every day.
This means that your resume needs to be machine-readable and score highly with this type of software.
But no, this doesn’t mean that you need natural language processing to develop your document.
In fact, what you need to do is include relevant keywords from the job description that will get you noticed and highly ranked by recruiters.
Furthermore, it’s important to save your resume in a simple file format that can be parsed easily by a machine.
This would be PDF or TXT formatting.
Instead, Word files might get botched when inputted into an ATS system, so it’s best to avoid them and to not get too out-of-the-box when coding your file.
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 an entry-level resume, you’ll need to gather your best programming or engineering achievements that show you have a machine learning engineer skill set.
💡 Top Tip
Transferable skills are abilities that carry over from one job to another. These are crucial for entry-level candidates to prove to employers that they can perform.
Hype up any past software engineering projects and responsibilities that are related to machine learning or that have transferable skills.
You can even just include assignments or extra courses which show teamwork, analytical thinking, or an affinity for the handling of data.
Does Your Education Section Sound Like a Spam Bot? Let’s fix it
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.
💡 Top Tip
Ensure you mention coursework relevant to the specific job you’re targeting. This will prove how your skillet fits in well with the role and can also help fill in gaps in employment.
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 PhD 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 high chances of leading to an interview, and another that will leave the candidate complaining in their favorite Discord channel.
PhD in Machine Learning
- Completed data mining project published on Hacker News.
- Top 1% of students in data mining and regression analysis.
- Research paper: Deep Residual Learning for Video Recognition, published in Journal of Artificial Intelligence Research.
MSc Degree in Computer Science
Santa Clara University
- Achieved a 3.6 GPA.
- Relevant coursework: machine learning, artificial intelligence practice, language and computation, information visualization, data-intensive systems.
BSc in Information Technology
Santa Clara University
- Achieved a 3.8 GPA.
- Member of Artificial Intelligence Student Club.
MSc Degree in Machine Learning
University of Washington
- Member of Student Arts Club.
- Worked at Wendy’s as a server.
The first of these examples will get you an “A” in the hiring process.
The second 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?
While the first example mentions extra information which will help the candidate get hired, the other one mainly includes details that are not related to the field.
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.
Your education section is another opportunity to propagate machine learning in your resume by including relevant pursuits you undertook at college.
The Best Machine Learning Engineer Skills for a Resume
If you’re looking to start a machine learning resume, it’s unlikely that you’re not the sharpest tool in the shed.
In reality, you’ve probably spent a whole 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 machine learning resume example, 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?
Ability to learn
Data modeling and evaluation
Natural language processing
Probability and statistics
How to Add Other Sections for an Effective Resume
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 professional associations, publications, awards, certifications, and any volunteer work which might be relevant for your application.
Been 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 be applicable to your future role.
Remember that it’s fundamental to optimize space on your resume, and sharing your passion for creating deepfakes of famous politicians with your friends doesn’t cut it.
On the other hand, hobbies, special achievements, or personal passions that can make you seem like a good fit for the position, are worthwhile additions.
Here are some well-made examples to follow, including relevant machine learning resume projects:
Machine Learning Projects
- Created face synthesis GAN-based model
- Developed Twitter sentiment analyzer
- Prediction of closed questions on Stack Overflow
- Winner of Kaggle Future Sales Predictor contest
Machine Learning Awards
- Finalist in the Computing AI & Machine Learning Awards
- Winner of the Global Annual Achievement Awards for AI
Machine Learning Certifications & Licenses
- Chartered Data Scientist (CDS)
- Certification of Professional Achievement in Data Sciences
- eCornell Machine Learning Certificate
- Certified Associate in Python Programming (PCAP)
Machine Learning Publications
- “Putting Machine Learning at the Heart of Health Care” published on MIT News
- “Neural Networks That Will Blow Your Mind” published on Scientific American
- “Hybrid ITLHHO Algorithm for Engineering Optimization” published in the Wiley renowned International Journal of Intelligent Systems
Machine Learning Associations
- Association of Data Scientists (ADaSci): managed member relations between 100+ machine learning engineers
- Association for the Advancement of Artificial Intelligence: organized two online webinars with at least 50 participants each
- Machine Learning Curriculum Development Volunteer at Stanford University
- Statistics Without Borders
Hobbies and Interests
- Blogging about AI
- Learning languages
Starting to get the right idea?
All these entries clearly add additional value to the candidate’s resume, either by showing transferable skills or pertinent experience.
Nevertheless, it can be difficult to decide which kinds of additional activities are worthwhile, and it’s not always as smooth as the machine learning project templates included above.
Here are some extra sections which may do you more harm than good when trying to emphasize machine learning on a resume:
- HubSpot inbound marketing certification
- ServSafe Gastronomy certification
Hobbies and Interests
- Listening to music
- Magic: The Gathering player
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 really 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.
If you’re struggling to come up with project ideas, you might find some within machine learning resumes on GitHub, or by searching through Kaggle or Stack Overflow.
You’ve learned about the perfect resume-building algorithm. Now it’s time to start creating your machine learning engineer resume pdf.
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
- Include any high-level education such as a PhD, MSc, or BSc, as well as any related academic or extracurricular accomplishments
- Incorporate any personal projects or publications related to machine learning
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