The year is 2052: you're taking a trip down memory lane. And in the not-so-distant 2022 everyone was hyped about machine learning (ML). That was also the year you started your ML internship.
The past three decades have provided you with many end-to-end project implementations. Augmented algorithms. Cutting-edge technological innovations in voice-to-text recognition.
"Shaken not stirred." A few seconds later, your Apple Home-Help Remote brings you a martini, while the heat of your fireplace is lowered.
This isn't something out of an Asimov novel or a Spielberg project.
If you haven't realized it yet, machine learning is not just a trend, but turning into a vital segment of industry-wide digitalization.
Remember how a few years back, cloud technology was catching on? Those early adopters are now way ahead of the game.
Experts predict that the same will happen with machine learning.
"The global machine-learning market is forecasted to grow to $209.91 billion by 2029 with a 38.8% compound annual growth rate (CAGR)."
As ML technologies mature, product development is becoming a necessity for all, creating many exciting career opportunities.
Unfortunately, it's a dog-eat-dog sort of situation out there. Everyone wants in.
Recruiters aren't making it easier. They're expecting you to have a Ph.D., at least 10 years of relevant experience, and to be under 30.
If you're a machine learning professional (just starting your career journey, or looking to progress), our guide will show you how to make the most out of your experience.
A clear and precise ML resume could help recruiters understand your specific skill set and your actual achievements. This plays a crucial role in the decision-making process.
So, before we get started, consider some of the following details:
- What technology (deep learning) or architecture you've implemented and its impact on the organization?
- How adopting research into your methodology has helped enhance the learning experience?
- If you’ve augmented algorithms, what sort of complex or crucial problems have you been able to solve?
P.S. Don't forget also about the vital machine learning operations (ML Ops) in production experience you may have.
5 million-dollar questions (and information) this guide will answer (and provide):
- If you still haven't got your M.S. or Ph.D. degree in machine learning, here are a few ideas on how to show recruiters they should take your application seriously!
- How to choose which items of your experience to include in your ML resume?
- Achieving the perfect balance between your supervised and unsupervised learning, and various other technical skills, with your communication and other soft skills.
- 20+ of the most popular (and acclaimed) machine learning certificates and a few FREE resources to boost your career
- How to list your projects to provide more depth to your ML experience?
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Writing your machine learning resume: step-by-step guide
The machine learning field is over-saturated with "experts", interns, and self-taught professionals. All are more than willing to work for pennies and seize any opportunity that comes their way.
To stand out from the crowd, you need, a clear, concise, and targeted resume. One, that meets both recruiters' and the applicant tracking system's (ATS) demands.
If you want to kick-start your ML career today, here's your strategy for success.
Step one is to consider how much experience you have, that’s relevant to the job. Simply put, weigh out your chances and focus on key requirements (in line with the requirements).
What recruiters want to see on your resume:
- What kind of deep learning/architecture have you implemented?
- Have you done any ML Ops work so far?
- How well can you develop or augment a given algorithm to solve a specific problem?
- Have you participated in end-to-end implementation and deployment of projects? What was your role through the process?
- Can you demonstrate relevant research you’ve done: did you have the chance to test it in a real-life project?
Step two is to adjust your resume formatting to the capabilities you'd like to highlight.
Choose:
- reverse chronological formatting to shine a light on key roles within your professional experience.
- hybrid or functional resume formats to get into the skills and technologies of your experience.
If you don't have 15+ years of experience that's really noteworthy, stick with the one-page resume format.
Your resume needs to show precisely what goals you accomplished in the big picture of things, using technologies/ skills XYZ.
Consider this formatting: "Did this, which produced this result, which helped achieve this for the company."
Or, put in practice: "Implemented logistic regression to ensure credit scores predictions are 65% more accurate, helping 200+ users take more educated decisions"
Once you have the basics down, step three is to tailor your resume for every machine learning position you apply for. Focusing more in detail on the skills and requirements.
As you might have expected, your technical skills would play a huge role in centering your machine learning resume. But most companies don't want to hire "bots", that's why you also need to show a human side.
Consider having a healthy mix of both soft and technical skills (we'll get into this in later sections).
Top machine learning resume sections:
- Objective (including keywords and achievements)
- Experience (both real world and academia)
- Education (focusing on your degrees)
- Skills (good idea to include a technical skills panel and strengths section)
- Projects (showcasing how you applied technologies to achieve success)
Ace your machine learning resume header with these four+ tips
Keep your machine-learning resume header brief and simple.
Meaning that it should help recruiters easily:
- find your relevant contact details
- preview your current portfolio of work via GitHub, LinkedIn, or a personal website
- hint at your niche within the headline.
Use the headline (or title) to reflect upon how far you're along your machine-learning career and what you plan on achieving.
Or it could be a good opportunity to showcase one of your biggest accomplishments. Remember that it has to be something really, really impressive and specific - not just "I automated some stuff".
You could also use the headline to mention three relevant job ad keywords within your expertise.
The best thing you can do is to let your title briefly hint at your relevant experience, without getting into too many details. That’s what the rest of your resume is all about.
You’ll find below two real-life examples of the right and wrong way to format your resume header.
2 Machine Learning Resume Header Examples
The first example doesn’t work as:
- the headline is vague
- there is no portfolio of work
While the second example, pinpoints a highlight in the candidate’s professional career.
The portfolio quickly and easily helps recruiters to get a better understanding of this ML engineer’s work.
Must-have five sentences as part of your machine learning resume summary
Your resume summary is a pretty neat section or tool to get recruiters' attention.
Use it to align your technical skills with the job requirements and show more of your people/ personal skills.
We'd recommend that within the up-to-five-sentence format, you could include:
- your current role, years of experience, and specific ML niche
- two successes detailing technologies you've used
- one humanizing element, focusing on your soft skills in communication, leadership, etc.
Your best bet would be to always tailor your summary (and your whole resume, actually) for the job you're applying for.
So, if the role requires you to "boost existing open-source libraries to support model training innovations", make sure to include a sentence about your experience.
Of course, only if it's relevant. Lying on your resume is a big NO!
Here's how you shouldn't and should write it:
2 Machine Learning Resume Summary Examples
This summary is way too simple. Yes, it does pinpoint the direction in which the candidate would like to grow, but it doesn’t focus on achievements.
Or provide any real substance, for that matter, as to who this person is and what they can achieve.
Check out the below example - we think it’s way better.
Breaking down the example above, you have:
- current expertise and niche
- technical knowledge (research and projects) to showcase ability working in diverse ML environments
- soft skills - analytical problem-solving and meeting deadlines
- goals for the future
Technologies and accomplishments in the experience section of your resume
Your machine learning resume can include a mix of practical and academic experience. Use it to demonstrate the challenges you faced, the actions you took, and the outcomes you achieved.
List each experience point via bullets that include a technology/ action + accomplishments.
Avoid vague and unclear tasks. And, please, don't overstuff your experience section with job advert keywords, just because it looks "good".
Recruiters are looking for the essence of your machine-learning experience. Nothing is more convincing than details.
So, here are a couple of questions you could answer, considering the technical aspect of the role.
If you've worked on:
- interactive visualization platforms: what language or tech stack did you use to build them?
- object tracking and depth estimation: what were the domains, objects, and models you implemented?
- mature learning pipeline: what languages, libraries, model(s), and/or applications does it consist of?
- k-means clustering implementations: did you do it from scratch? Be specific about the languages and if you've used scikit learn.
The more specific you can get about the technology, the better you'd qualify your expertise and knowledge.
Just don't forget to frame each bullet with the big picture things - the outcome of your actions, in relation to the department, company, etc.
This is one of the best ways to tell the story of your success. Rather than bragging about your expertise in Random Forests, show how you've applied the algorithm to better predict prices.
If you've many experience items, curate the best (and most relevant) ones at the top of your resume.
Continue to list the rest in an "others" section to show that there are no gaps in your experience.
Machine learning resume experience examples
02/2018 - 11/2021 
- •Solving real-world problems by developing machine-learning solutions
- •Researched the need for better predictive models and data modeling techniques
- •Implemented new models
- •Helped production with algorithms
This resume doesn’t specify the:
- technologies used
- outcomes and purpose of each individual task
- actual competencies of the engineer
02/2018 - 11/2021 - •Researched how the performance of renewable energy equipment features could be 35% more efficient clustering and association to provide product improvement recommendations
- •Developed 12 individual predictive models and 30+ data modeling techniques, so that production becomes 65% more effective
- •End-to-end implementation of 6 new models (from data cleaning to cloud deployment) which helped track the performance of 1k+ generators and panels
- •Understood the needs of 6 engineers and built an algorithm (using Python) to help them tackle and resolve 250+ production challenges
Each bullet within the example above follows a task, technologies, outcome structure.
Showcasing not only the breadth of technical skills but also a problem-solving and goal-oriented approach.
Who doesn’t want to work with a professional with such a mindset?
Entry-level machine learning resume tips
Machine learning is a crazy hard job market to break into, with the rejection rate at the junior level reaching an all-time highest.
What most recruiters want to see on your resume is at least a Ph.D., relevant research, and/or practical experience (of at least 10+ years).
This section of our guide is especially for those B.A./B.S. graduates, who still haven't given up hope and want to leave their footprints in the ML world.
So, how do you curate your entry-level resume to kick-start your ML career?
Make sure you include your data or computer science degree.
Even though it's below the bare minimum requirements, your degree is still a good shout-out for your technical skills and knowledge.
Moving on to your experience section: only select roles that are relevant to the job.
Instead of detailing the achievements of your 9-5 office admin job, focus your experience section on your academic research experience. You'll thank us later.
You still have a decent shot at landing a role in an ML team, if you have a background in
- software engineering
- data engineering
- data science.
Here's another joker to improve your chances. Take an extracurricular course in machine learning or do some independent study.
There are some free resources out there like
- MIT's free video lectures on ML for Healthcare
- Google or Kaggle's training session in data sets for AI
- "Learn Python 3 the Hard Way"
Another good entry-level experience to gain is in a startup environment. This would give you more hands-on experience and opportunities to experiment.
Startups are very much culture-focused, so apart from your technical skill set, make sure you also mention who you are and what you stand for.
And in a couple of years' time, you'd be ready for that MAANG (Meta, Amazon, Apple, Netflix, Google)-level job.
Even if you do get rejected, don't get your hopes down. This is one of the trendiest (and most sought) jobs at the moment.
Experienced professionals in machine learning: resume tip
If you're in the ML field, you know that it's mostly dominated by M.S. and Ph.D. graduates (no surprise there).
Keep this in mind when curating the experience section of your resume.
Some of your past machine learning roles may be more junior, and in the end, you could decide to dedicate less space to them.
For this industry, it's important to show your research experience. You can include details about your successful experiments, publications, and awarded grants.
As always, remember to keep your experience relevant to the job you're applying for.
Balancing technical machine learning skills with soft skills
As you might have expected, your technical capabilities play a huge role in the job application process.
To make a good impression on recruiters, dedicate a separate section to explain your technical or hard skills.
Within it, you could detail your:
- higher math skills
- the different types of machine learning you've implemented (with all the specific libraries and details)
- programming languages you excel at.
You may also choose to create a specific section for your research/ projects in supervised vs unsupervised learning. Make sure you list not only the algorithms but also the end results.
E.g. “tested predictability model for customer churn using logistic regression, decision tree, and XGBoost - improved forecasting abilities by 75%”
Select technical skills to show a complex mix of your experience in programming, statistics, and perhaps a few Business intelligence (BI) tools.
What's more, recruiters do care about the UVP (unique value proposal) you can bring about.
This is reflected in who you are, what you stand for, and your goals for the future.
But we'll get into this in the next section.
For now, find out what are the:
Top 50+ machine learning technical skills and technologies:
- Deep Learning
- Neural Network Architecture
- Database Querying
- Deploying Model in API
- Django/ Flask
- Natural Language Processing
- Audio and Video Processing
- Advanced Signal Processing
- Algorithms
- Supervised Learning
- Linear Models
- Linear Regression
- Logistic Regression
- Ridge Regression
- Lasso Regression
- Three-Based Models
- Decision Tree
- Random Forests
- Gradient Boosting Regression
- XGBoost
- LightGBM Regressor
- Unsupervised Learning
- Clustering Models
- K-Means
- Hierarchical Clustering
- Gaussian Mixture Models
- Association
- Apriori Algorithm
- Automation
- Data Science
- Databases
- Handling Multidimensional and Multi-Variety Data
- Programming Languages
- Python
- R
- C++
- Javascript
- SQL
- Distributed Computing
- Cloud Computing
- Computer Engineering
- Computer Science
- Linear Algebra
- Calculus
- Statistics
- Trends and Patterns
- Software Engineering
- Tableau
- Power BI
- AWS
- Google Cloud
How to describe soft skills on your resume
At the very beginning of this article, we did mention the humanizing factor of machine learning professionals.
Or the set of soft skills you have. Those transferable capabilities you've attained thanks to your experience or education. Soft skills basically hint at who you are as a person/ professional.
And at the end of the day, recruiters do choose the candidate that would be the best fit for the company culture. This is important as the lower the turnover rate, the happier the teams are.
You can dedicate a separate strengths section on your resume to detail why you’re the right person for the job:
30 machine learning soft skills:
- Dedication
- Depth of Machine Learning Knowledge
- Attention To Detail
- Always Seeking To Improve Current Systems
- Enriching Products/ Projects/ Teams By Sharing Knowledge
- Desire To Learn
- Analytical Thinking
- Critical Thinking
- Succinct Problem-Solving
- Decision-Making
- Ability To Communicate Ideas Across
- Active Listening
- Collaboration
- Teamwork
- Cooperation With Professionals From Different Fields
- Open-Mindedness
- Outside-The-Box Thinking
- Creativity
- Time Management
- End-To-End Project Management
- Persistence
- Facing Challenges
- Learning From Experience and Failure
- Willingness To Experiment and Test
- Conscientious
- Work Ethics
- Patience
- Multi-Tasking
- Innovative Mindset
- Future-Facing
No M.S. or Ph.D. in machine learning? Here’s how to curate your education section
Machine learning is one of those industries that really cares about your educational background.
Looking at adverts, you'd notice that a master's or doctoral degree is the bare minimum requirement to kick-start your career.
But what if you…
…want to take a shot at the industry with just your bachelor's?
Make sure you've listed all relevant higher education degrees. And supplement those with research or projects.
You could dedicate extra space to your
- end-to-end implementation projects
- unsupervised learning algorithm augmentations
- research into perfecting voice-to-text recognition applications
The list can go on and on, but let's look at a second most common-case scenario when it comes to your education.
…are currently completing your M.S. or Ph.D.?
Include this information in your education section with expected graduation dates.
This shows your persistence in learning more about the ever-evolving world of ML.
Are machine learning certificates a must?
Anyone can tell you that with the fast-paced evolution of the ML industry, today's technology is already old news.
Due to the field's technical nature, a growing skill set is a must. That's where the role of certification and courses kicks in.
So, should those be included in your resume?
Definitely!
Certificates show relevancy, industry interest, and persistence. They hint to recruiters at plenty of other soft skills, which are good to have.
The thing to remember is that there are so many options out there. You should look for industry-recognized certifications, instead of completing a bunch of certificates for the sake of it (or as resume fillers).
Here are the 20+ most popular certificates you can take to boost your ML career today.
Top 24 certificates for your resume:
- Supervised Machine Learning: Regression and Classification - DeepLearning.ai and Stanford (offered by Coursera)
- Deep Learning Specialization - DeepLearning.ai (offered by Coursera)
- Machine Learning Engineering for Production (MLOps) Specialization - DeepLearning.ai
- Machine Learning with Python - IBM (offered by Coursera)
- Machine Learning Professional Certificate - IBM
- Machine Learning Crash Course — Google AI
- Data Engineering, Big Data, and Machine Learning on GCP Specialization - Google
- Professional Machine Learning Engineer - Google
- Azure AI Engineer Associate - Microsoft
- Machine Learning — EdX
- Introduction to Machine Learning for Coders — Fast.ai
- Machine Learning Engineer for Microsoft Azure Nanodegree - Udacity
- AWS Machine Learning Engineer - Udacity
- Machine Learning DevOps Engineer Nanodegree - Udacity
- Machine Learning Scientist - DataCamp
- Machine Learning Scientist with R Career Track - DataCamp
- Machine Learning Fundamentals - Dataquest
- Machine Learning Engineering Career Track - Springboard
- AWS Certified Machine Learning — Specialty
- Get Started with Machine Learning - Codecademy
- Professional Certificate in Data Science - Harvard
- Machine Learning - Stanford University
- Applied Data Science With Python Specialization - University of Michigan
- eCornell Machine Learning Certificate - Cornell University
Projects section to highlight remarkable industry achievements
Your machine-learning resume is about those tiny details that make your experience worth it.
Use the projects (or research and publications) you've worked on to shine a better light on your expertise.
Having a separate section on your resume, dedicated to up to three most noteworthy projects, could make an awesome impression on recruiters.
Thus, you’d be helping them to understand what technologies you are apt at using and what you can actually achieve.
Describe your past projects concisely with the STAR method:
- situation or task: what was the problem you faced?
- actions: how did you resolve it using your technical or soft skill set?
- results: it's all about the numbers, numbers, numbers!
One more heads-up: select only projects that are relevant to the job you're applying for.
As ML keeps on evolving, your work from 10-years-ago may use older methodology/technology and may no longer be as impressive as it was back in the day.
Key takeaways
- Your machine learning resume should focus on the problems you resolved: the technologies, architecture, and algorithms you implemented and their impact on the organization.
- End-to-end implementation and ML Ops are just as valuable experience points as your academic background and research.
- The resume summary should include your years of experience in machine learning, two technical achievements, and one humanizing element.
- Curate various parts of your resume (experience, certificates, projects, and strengths) to pinpoint both your technical and people/ personal skills and the impacts you've made.
- If you're an entry-level B.S. engineer, looking to get into the industry, focus on the technical aspects of your experience and courses. Also, look for startup opportunities to grow your career.
