Machine Learning Engineer CV Examples & Writing Guide

A strong machine learning engineer CV doesn’t just list your technical skills. It showcases how you apply them to deliver real-world results. In the fast-growing UK tech sector, you need to show your ability to design, build, and deploy ML systems that solve business problems. 

From your professional summary to additional sections, this guide shows you how to structure your ML engineer CV. We’ll begin with a sample mid-level machine learning engineer CV, then break down each section with advice and examples.

This guide will show you:

• A machine learning engineer CV sample that outperforms 9 out of 10 other CVs.
• How to craft a CV for a machine learning engineer that will secure you more interviews.
• Tips and examples for showcasing skills and achievements on a machine learning engineering CV.
• How to describe your experience in an ML engineer CV to secure any job you desire.

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Machine learning engineer CV example

Looking for some other CV samples? Visit our guides:

• Data Analyst CV Example
• Computer Science CV Example
• Software Developer CV Example
• Software Engineer CV Example
• IT CV Example
• Project Manager CV Example
• Business Analyst CV Example
• Web Developer CV Example
• Career Change CV Example
• Other CV examples

Let’s see how to write an ML engineering CV that’s just as impressive as the one above.

1. Format your machine learning engineer CV correctly

How you format your CV is just as important as its content. Recruiters seek a clear structure and organisation that showcases both your technical expertise and project achievements. A well-formatted ML engineer CV demonstrates your ability to present complex information clearly. And that’s an essential skill for engineers who need to explain machine learning solutions to diverse teams.

📝 Here’s how to structure a CV for a machine learning engineer:

• Follow the standard CV layout for better readability. Include sections such as:
• CV header (name, title, contact info)
• Personal profile (a summary or an objective)
• Work experience
• Education
• Skills
• Additional sections (certifications, awards, projects).
• Choose a clear CV font, such as Arial, Calibri, or Times New Roman, and keep its size between 10 and 12 points for the main text.
• Set clear margins and spacing. Use 1-inch (2.5 cm) margins and 1.15 line spacing to ensure the CV is easy to read.
• Keep appropriate CV length. A mid-level machine learning engineer's CV can be 1–2 pages, depending on experience.
• Use a descriptive file name. Save your CV as a PDF (unless otherwise specified) with a name like Firstname_Lastname_MLEngineer_CV.pdf. PDFs preserve the layout across devices.

💡 Read more: What Makes a Great CV

2. Highlight your work experience in a machine learning engineer CV

Machine learning roles are results-oriented, so your work experience section must highlight the genuine impact of your work. That’s why you need to ensure this section impresses your recruiter with measurable achievements.

When describing your machine learning experience:

• Use a reverse-chronological format, starting with your most recent role and working backwards to show your recent activities and role development to potential employers.
• Tailor your CV to the job you’re after. Do it by emphasising the skills and projects most relevant to the role you’re applying for.
• Include full details. Under each entry, list the company name, location, and your title, along with dates of employment.
• Focus on your accomplishments, not your duties. 
• Add bullet points for clarity. They make scanning your achievements much easier, which saves a lot of time.
• Begin each bullet point with an action verb, such as developed, implemented, optimised, and improved.
• Quantify results. Whenever possible, include metrics or numbers (e.g., percentage improvements, cost savings, revenue impact) to support your claims.

Let’s look at examples of how to write the experience section for an ML engineer:

Machine learning engineer CV examples: experience section

The right example highlights specific, measurable achievements that clearly demonstrate impact. The wrong one is vague, listing duties without context or results. The first impresses, while the second offers little about the candidate, as all these duties are rather generic.

And what if you’re transitioning into machine learning with limited direct experience? Focus on transferable skills, relevant projects and technical abilities:

Machine learning engineering CV with no experience

As you can see, building an impressive CV without experience is achievable. The right example emphasises relevant technical skills and teamwork contributions (such as data projects, process improvements, and early ML work) even without an official ML title. Conversely, the poor example is too generic. By concentrating on skills and outcomes, even a candidate with less ML-specific experience can persuade hiring managers to give them a chance.

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3. Show education on your ML engineer CV

Machine learning is a technical field, so your academic background matters. Listing your educational background can easily demonstrate your foundation in computer science, mathematics or data science. It’s a CV section you don’t want to skip.

Include in the education section:

• Degree details. State your degree, major (e.g., M.Sc. in Machine Learning), institution, and years.
• Relevant courses or projects. Mention classes or projects that directly relate to ML (e.g., Deep Learning, Data Mining, Neural Networks).
• Honours and awards. Include GPAs or ranks if notable (e.g., First Class Honours, Dean’s List, Distinctions).
• Thesis/dissertation. Briefly note the title if it’s relevant (especially if it involved a significant ML project).
  
  

Let’s see an example of how to do it right:

ML engineering CV example: education section

It’s perfect, as it highlights not only top-tier UK universities, but also relevant coursework. The dissertation title demonstrates a specific ML focus, and the honours designation underscores academic excellence.

💡 Pro tip: Avoid listing irrelevant or overly basic education details. For instance, just writing “IT Diploma – 2010” or “Completed online course” without context doesn’t add value. Instead, tie your education directly to your ML skills and achievements.

4. Highlight relevant skills in your machine learning engineer CV

Your ML CV’s skills section should showcase both your technical expertise and your ability to collaborate and solve problems. Ideally, this section reinforces what you demonstrate in your experience and education sections, while blending your soft skills with your hard skills.

Key skills for machine learning engineers include:

• Python, R, or Java programming
• Machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Deep learning frameworks (TensorFlow, PyTorch, Keras)
• Data preprocessing and feature engineering
• Natural Language Processing (NLP)
• Computer vision techniques
• SQL and NoSQL databases
• Big data tools (Spark, Hadoop)
• Model deployment (Docker, Kubernetes, cloud platforms)
• Version control (Git, GitHub)
• Active listening skills
• Analytical skills
• Communication skills
• Interpersonal skills
• Decision-making skills
• Computer skills
• IT skills
• Problem-solving 
• Analytical thinking
• Attention to detail 
• Communication skills 

And here’s how these skills can look on a CV:

ML engineering CV example: skills

List a skill category, then a brief description of your proficiency or experience. It turns abstract skills into concrete statements, which is more compelling than just a bullet list of buzzwords, and will surely give you an advantage over your competitors.

💡 Read more: Key Skills for a CV

5. Put additional sections on your machine learning engineer CV

Once the core sections are covered, adding relevant extras can make your CV more memorable and help it stand out. Plus, a well-crafted additional section will reinforce some of the abilities you listed in your skill section. However, remember to include extra sections only if they strengthen your application as an ML engineer. As always, relevance is key.

Here are some applicable additional sections for an ML engineer's CV:

• Certifications: e.g., AWS Certified Machine Learning, Google’s TensorFlow Certificate, or other reputable data science credentials.
• Projects: If you have significant personal or open-source ML projects, list them (especially if you’re new to professional experience).
• Publications or conferences: Any published work or conference presentations on ML topics.
• Awards and honours: Machine learning competition wins, scholarships, or hackathon prizes.
• Volunteer work: Contributions to ML communities, mentoring, or volunteer teaching on tech topics.
• Hobbies and interests: However, only those relevant to your field, such as tech blogging, participating in hackathons, public speaking, and open-source contributions, etc. 
• Language skills: Especially if relevant for global teams or research (e.g., English, Spanish).

Let’s see how it’s done:

CV for ML engineer examples: extra sections

The right example lists things that are strictly related to machine learning. The wrong one lists vague (language without skill level proficiency) and entirely unrelated items, making it unimpressive and generic.

💡Pro tip: Lying on your CV is a serious mistake that can cost you not only your credibility, but also your chance of securing a job with the company you apply to.

6. Create a perfect machine learning engineer CV summary

Your CV summary is the first thing recruiters read. It should quickly convey who you are, what you’ve achieved, and what you bring to the role. Think of it as a concise paragraph that highlights your strongest qualifications for this ML position.

To write a compelling summary, you can use this formula:

Adjective + Machine learning engineer + Years of experience + Key achievements + Key skills + What you want to do for the employer 

Let’s compare these examples:

Machine learning engineer CV samples: profile

The right summary is precise and results-focused: it highlights years of experience, tangible achievements (accuracy improvements and savings), technical skills, and a clear emphasis. The wrong summary is vague, lacking evidence or specifics. And it’s simply poorly written: me, me, me. Your potential employer wants to see your focus on what you can bring to the table, not on yourself.

💡 Read more: CV summary examples & writing tips

7. Add a cover letter to your machine learning engineer CV

Adding a cover letter to your CV is a must, since 89% of recruiters expect candidates to submit them, with a staggering 81% saying they have rejected candidates solely on the basis of their cover letters. So walk an extra mile and make it complement your CV by telling a brief story of your career and passion for the field.

Here’s how to structure a cover letter:

1. Open your cover letter with a hook that grabs attention, for instance, a brief story of a relevant accomplishment or your enthusiasm for the company’s mission in AI/ML.
2. Include all essentials. Add your contact information, a professional greeting, and ensure you clearly state how hiring you will benefit the company.
3. List some of your most impressive achievements. Use bullet points to make them stand out more and to boost the readability of your cover letter.
4. Keep it concise. Aim for no more than one page (around 250–300 words). Be clear and focused; every sentence should add value.
5. Conclude your cover letter with a call to action. Reaffirm your interest and suggest next steps, such as stating you’ll follow up or providing your availability for an interview.

Plus, a great cover letter that matches your CV will give you an advantage over other candidates. You can write it in our cover letter builder here. Here's what it may look like:

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See more cover letter templates and start writing.

Key takeaway

Now that you know everything about having an impressive machine learning engineer CV, it’s time for a quick recap:

• Ensure you follow the correct CV format to keep your document readable.
• Start with a strong summary, but write it last. Everything you need to create it is already in your CV.
• Focus on your achievements rather than your duties, and list them in bullet points, starting each with an action verb.
• Detail relevant education, including degrees, honours, and coursework or projects that underpin your ML expertise. 
• List a mix of key technical and soft skills. Incorporate relevant keywords from the listing to make sure these abilities align with the job requirements.
• Add relevant extra sections, such as certifications, projects, publications, or awards. This can be an easy way to back your soft skills.
• Always include a cover letter with your CV. Not having one could create a poor first impression on your recruiters.
  
  

With your CV polished and tailored, you’ll present yourself as a skilled machine learning engineer ready to deliver results. Good luck with your job search!

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Sources

• Christof Nägele, Barbara E. Stalder , "Competence and the Need for Transferable Skills"
• Joel Schwartzberg, "Subscribe Sign In Latest Magazine Topics Podcasts Store Data & Visuals Case Selections HBR Executive Resumes Cover Letters Still Matter—Even If They’re Not Required"