Machine Learning Resume: Tips, Examples, and Writing Guide

When searching for a job in AI, you'll want a well-crafted machine learning resume to help you stand out from the highly competitive (and crowded) pool of applicants. A great resume can highlight your skills and experience and clearly demonstrate why you'd be a great fit for the machine learning job to which you're applying.

But what do you need to do to effectively translate your experience, skills, and achievements into a compelling resume? And how do you boil it all down to just a page or two of material?

From proper formatting to identifying key skills and using impactful action words, read on to learn how to craft a machine learning resume that actually gets noticed.

Machine learning resume tips

Crafting an eye-catching machine learning resume requires careful planning, consideration, and attention to detail. Tailoring your resume is key, along with choosing an easy-to-read professional format that allows you to include all your relevant skills, experience, and achievements. Below are additional resume-building tips to consider.

1. Tailor your resume to the industry in which you'll work.

Machine learning is a broad field composed of many different roles within a wide range of industries. When first starting your machine learning resume, it's important to first identify the exact type of role to which you'll be applying and the industry in which you'll be working.

By understanding your niche, you'd better be able to tailor your resume to both the industry and the type of job to which you'll be applying. Common industries for machine learning jobs include retail, technology, health care, and financial services. Common jobs include:

• Machine learning engineer

• Data scientist

• Robotics engineer

• Software developer

• Artificial Intelligence (AI) engineer

• Cybersecurity analyst

Once you've effectively identified the job and industry in which you'll work, you can better tailor your resume.

2. Format your resume to highlight what matters.

The key to strong resume formatting is the same as for most designs: Keep it simple.  Lay everything out as clearly as possible. Listed below are all the sections you should include in your machine learning resume from top to bottom:

• Header: Your header includes your name and contact information, including a link to your LinkedIn profile if you have one. 

• Summary: Your summary is a short bio summarising what is to come in your resume. 

• Experience: This section is critical because it is here that you show you have relevant machine learning experience, listed in reverse chronological order. 

• Education: List your education here, with your highest qualification at the top. 

• Skills: Include skills mentioned in the job description for the role you’re applying for. 

What about fonts?

It's easy to love a fun font, but when it comes to a resume, it's best to keep things strictly professional.

For the body text of your resume, use an easy-to-read font like Times New Roman, Arial, or Helvetica in 11 to 12 point font size. For Section headings, opt for bold 14 to 16 point font size.

3. Craft a resume summary or personal statement.

Your summary is a short bio (four to seven lines) providing an overview of who you are, your relevant skills and experience, and what you can bring to the position. On your machine learning resume, you'll use this section to highlight the key reasons you're an ideal fit for the job you're applying for.

This is your sales pitch, and how well you write your summary will likely determine if a recruiter will read on and if you will be shortlisted for the job. A strong summary typically includes the following information:

• Experience: Identify relevant prior experience you may have had.

• Impact: Identify the concrete impact you had at work.

• Skills: Indicate skills relevant to the position that you may possess.

Example

Senior machine learning engineer with five years of experience developing machine vision within a start-up environment. Extensive experience in deep learning and deep reinforcement learning, and natural language processing.

4. Highlight your machine learning experience.

The experience section of your resume is crucial to demonstrating your prior work accomplishments and highlighting your relevant skills. This is where you'll list all your previous related jobs, including your job titles, the companies at which you worked, the dates of your employment, and describe the relevant duties you performed.

When describing your prior work experience, emphasize your concrete achievements. This way, recruiters will get a sense of how you actually performed in your previous jobs. The approach also provides hiring managers with a clear indication of your potential performance in the position being offered.

To craft concrete achievements in the work experience section of your machine learning resume, use the STAR method. STAR stands for “Situation, Task, Action, Result.”  For every bullet point in your experience section that demonstrates an achievement, think about the situation (your role), the task (the overall goal), the action (what you did to reach this goal), and the result (the measurable outcome).

Example

• Developed personalised algorithms, increasing product usability by 45 per cent.

• Led a team of seven programmers in the development of three robotics prototypes utilising machine vision.

Use resume keywords to be ATS-compliant

Today, most recruiters use an applicant tracking system (ATS) to manage all the job applications sent for an open position. Whilst these systems can be useful in finding applicants with specific skills, they can also inadvertently weed out qualified applicants simply because they don't have the right keywords.

To improve your chances of being pushed to the top of the pile, strategically use keywords related to the position throughout your resume. You can identify relevant keywords on the job posting, which will use particular phrasing to describe the work you'd be doing in the job. Use this language to guide how you describe your own past experience.

5. Emphasise your education.

Machine learning engineers are highly educated. A bachelor’s degree is the absolute minimum requirement, but most have a master’s degree or even a PhD in a related field. Include bullet points to show achievements on specific projects, excellent grades, awards, recognition, and scholarships. You could also highlight any local or national honours.

6. Include the right machine learning skills in your resume.

The skills section is important in any resume, but specific machine learning resume skills are in-depth, technical, and essential to any position. To ensure you’ve listed the relevant machine engineering skills, look through job descriptions and include everything essential to tailor your resume and make it ATS compliant. 

Technical skills you might include:

• Data modelling

• Clustering algorithms

• Programming

• MATLAB

• Java 

• Python

• Principal component analysis

• Support vector machines

Workplace skills you might include:

• Communication

• Problem-solving

• Time management

• Decision-making

• Critical thinking 

7. Consider adding other sections to your machine learning resume.

You may want to include several other sections on your resume, depending on what you’re applying for, your experience level, and additional projects and awards you could acknowledge.

Some additional sections you may put on your machine learning resume include:

• Title: A title or headline can help reinforce your professional identity. For example, under your header, you may add the title “Machine Learning Engineer” or “Machine Learning Engineering Specialist for Infosys.” Doing this adds extra keywords and tells the employer you are already functioning at this level.

• Projects: A project section could help recruiters have a better understanding of your prior experience. This may be particularly helpful to students who have the skills but haven't performed them professionally yet. Be sure to add links to your GitHub or GitLab project pages.

• Publications and conferences: If you studied at the PhD level, you might have relevant publications to list. You may also add any notable industry conferences you participated in.

• Professional associations: Being part of a professional association can show you are serious about machine learning as a professional career. For example, you may note that you're a member of the International Neural Network Society (INNS).

• Licences and certificates: If you've obtained certification or licensure or completed a related non-accredited course, you may create a subsection for them on your resume.

• Awards: This is a great place to highlight any awards you have won, either personally or as part of a team at university or work. 

• Volunteer work: If you have had any volunteer roles that demonstrate your workplace or technical skills, you can include them in this additional section.

8. Write a machine learning cover letter.

Once your resume is ready, you'll need a robust machine learning cover letter.

Tailor the cover letter to the position you’re applying for, the company, why you’re seeking the position, and why you’re a good fit. The body of the letter should further elaborate on your relevant skills and experience. Keep the tone professional and the whole letter to just under one page. Focus on what you can offer and finish with a call to action.

Next steps

A machine learning engineer specialises in building and refining algorithms and models that turn data into actionable insights. Considering the complex nature of the role, machine learning professionals need to have a well-developed technical skill set, extensive knowledge of the field, and a strong academic background. If you're looking to land a new machine learning job, consider taking a relevant Specialisation or Professional Certificate through Coursera.

In DeepLearning.AI's Deep Learning Specialisation, you'll build and train neural network architectures like Convolutional Neural Networks (CNN), and have the opportunity to master theoretical concepts and their applications using Python and TensorFlow.

Google's Advanced Data Analytics Professional certificate, meanwhile, can help you learn in-demand skills like statistical analysis, Python, regression models, and machine learning.