ML Engineer Resume Guide and Templates

Below, we'll show you how to write the perfect machine-learning resume.

You can write it from scratch or use our resume templates from candidates who got the job.

• Write a resume that attracts more interviews.
• Highlight the most relevant projects that align with the goals of the role you're applying for.

TL;DR

• A well-structured ML engineer resume:
  • has detailed, hands-on data experience,
  • highlights projects and impacts,
  • and includes Technical Skills and Relevant Education sections.
• Customizing your resume for each job application is essential.
• Balancing technical skills with soft skills, such as communication and critical thinking, boosts your resume.

Template: Senior MLE

Why this resume works

This resume strikes a perfect balance of technical depth, measurable achievements, and effective storytelling, making it compelling for technical hiring teams; moreover, the candidate successfully transitioned into a Senior MLE role at a FAANG company recently.

1. Skill-Centric Structure: Each bullet begins with a core machine learning skill (e.g., Model Lifecycle Management, Recommendation Systems), immediately highlighting the candidate’s technical expertise. This makes it easy for hiring managers or recruiters to assess qualifications at a glance and ensures alignment with job descriptions.
2. Quantifiable Impact: The resume consistently demonstrates impact with metrics, such as:

• "Improved performance by 20% and cut manual labeling costs by 70%."
• "Boosted retention rates by 30%."
• "Cut turnaround time by 200x."

These metrics provide concrete evidence of contributions that hiring managers value over generic claims.

3. Demonstration of Leadership: Leadership capabilities are highlighted through examples of team management, cross-functional collaboration, and infrastructure development:

• "Led a team to design a context-aware application recommendation model."
• "Built shared tools and architectures... reducing project costs."

These show the candidate’s ability to lead technical initiatives and work effectively across teams.

4. Strategic Use of Tools and Technologies: The inclusion of relevant tools and technologies (e.g., Docker, TensorFlow, PyTorch, AWS) signals the candidate’s proficiency in industry-standard platforms, reinforcing their readiness to contribute to modern engineering environments.

Template: MLE Career Pivot

Why this resume works

1. Focus on Impact and Metrics: Each bullet point under Professional Experience includes metrics or results demonstrating the candidate's contributions in quantifiable terms.

• Model Development: Built and deployed predictive models to enhance customer segmentation and targeting, increasing engagement by 15%.
• Data Engineering: Streamlined data pipelines, reducing model training time by 40%.

2. Core Skills Emphasis: Each bullet begins with the core skill relevant to the achievement, immediately signaling the candidate’s expertise.
3. Tailored Scope Descriptions: Each role begins with a "Scope" bullet that provides context, helping readers understand the scale of the work and the environment.
4. Technical Skills Highlighted: The skills section outlines the candidate’s competencies, ensuring alignment with job requirements.

Core Skills 

These are the skills entry-level and senior-level candidates tend to have on their resumes.

Entry-Level ML Engineer

1. Programming Skills: Proficiency in Python, particularly with libraries like scikit-learn and pandas.
2. Mathematics and Statistics: Basic understanding of linear algebra and probability to implement simple ML models.
3. Data Handling: Experience cleaning datasets and performing exploratory data analysis (EDA).
4. Machine Learning Fundamentals: Familiarity with key algorithms like regression, k-means, and decision trees.

Senior-Level ML Engineer

1. Advanced ML Expertise: Proficient in designing and optimizing deep learning models for tasks like NLP or computer vision using TensorFlow or PyTorch.
2. System Design: Expertise in building scalable and robust ML pipelines integrated into production systems.
3. Big Data Handling: Skilled in managing large datasets using Spark or similar distributed computing frameworks.
4. Deployment and Monitoring: Experience deploying models at scale and setting up monitoring systems for performance and reliability.

Key Elements

Here’s what every great resume should include:

• Contact Information: Include your name, location, email, and links to your LinkedIn or GitHub profiles at the top, in no more than two single-spaced lines
• Professional Summary: Include a brief summary that highlights your key achievements and skills and explains who you are, what you do, what you’re known for, and where you’re going next.
• Work Experience: Highlight your most relevant roles in reverse chronological order, focusing on measurable achievements.
• Past Projects: Showcase the projects that show off your data analysis and modeling skills. Align your relevant projects with the products or services you're targeting.
• Skills: Emphasize the technical skills and tools relevant to the job you’re applying for.
• Education: Include your degree(s) and any relevant coursework or certifications, particularly if you’re a recent graduate.

Formatting

The format of your resume is just as important as its content.

• Use Reverse-Chronological Order: List your most recent work experience first.
• Keep it Brief: Use a single page. You need to be brief and articulate your work's depth and breadth. As a best practice, limit your resume to one page for less than five years of experience. With over five years of experience, expand to two pages, with most of your accomplishment bullet points emphasized on your two most recent work experiences. 
• Be ATS-Friendly: Ensure your resume passes through Applicant Tracking Systems (ATS) by using simple formatting and relevant keywords embedded throughout it (Core Skills, Technologies, Frameworks, etc.). Otherwise, it may never reach a hiring manager.
• Professional Design: Use a simple, professional format with easy-to-read fonts like Arial or Calibri.

Professional Summary

Like the first question in an interview, the summary sets the tone of your resume.

Your ML engineer resume summary should act as a personal pitch, briefly summarizing your background and experience relevant to a single role in 3 to 5 sentences. 

The summary framework includes: 

• Who you are 
• What you do 
• What you’re known for
  • This is a significant career accomplishment. (e.g., "Led, developed, and launched X product into a new target market, resulting in X% market adoption rate and $XXX revenue."
• Where you’re going next (Your target role – function and/or industry)

Focus on your notable skills and past achievements, such as in scalable/complex model deployments and building scalable and robust ML pipelines.

Example: Machine Learning Engineer with a strong background in software engineering and data analysis, focused on developing scalable models to solve complex problems. Known for optimizing algorithms, including a recommendation system that boosted user engagement by 30% and increased revenue by $1M. Seeking a Senior ML Engineering role to drive innovative solutions in the tech industry.

Work Experience

This section focuses on your achievements rather than simply listing job duties.

Align your accomplishments to the core skills of your target role. Use a skill: accomplishment framework to allow your resume to read like a job description, aligning your skills and accomplishments to most job descriptions. 

Show your direct impact on core KPIs like revenue, growth, or retention.

• Developed machine learning models: Improved customer segmentation accuracy by 25%, leading to a 15% increase in targeted marketing effectiveness.
• Optimized algorithms for scalability: Reduced model training time by 40% through efficient parallel processing techniques.
• Implemented deep learning techniques: Created an image recognition model with 92% accuracy, improving product categorization in e-commerce.

With most recruiters only reviewing resumes for 6-8 seconds, your two most recent work experiences should have the most accomplishments, about 5-7 bullet points each.

However, if older positions are particularly pertinent, you can include them, too. 

Past Projects

Highlight projects that demonstrate your technical skills and problem-solving abilities.

For example, link to a GitHub repository of an open-source project you worked on.

Include details like the tools and languages used (e.g., Python, TensorFlow) and the outcomes of your work. As you gain more experience, this section will become truncated.

Technical Skills

Tailor your skills list to the job description, focusing on the languages, frameworks, and workflows the team is hiring for. It's better to highlight fewer skills than to overstate your abilities. List your best skills first because hiring managers often assume the skills listed first are the ones you’re most comfortable with.

Common technical skills to include on an ML engineer resume include: 

• Programming Languages: Python, R, Java, and C++ are essential for building and optimizing machine learning models.
• ML Frameworks and Libraries: TensorFlow, Keras, PyTorch, and Scikit-learn are widely used for model development and experimentation.

Additionally, map your technical skills to your work experience where applicable. Embedding technical skills across your resume will also improve searchability on LinkedIn and in applicant tracking systems.

Soft Skills

Soft skills are just as important as technical skills in machine learning. You'll be expected to work cross-functionally and explain your findings to product managers, engineers, and business leaders. Articulating how you collaborate will make the difference between being considered for mid-career to senior-career level roles. 

In your work experience, highlight your communication, critical thinking, creativity, and project management skills.

Intangible skills for MLEs:

• Soft Skills: Communication, teamwork, and problem-solving abilities.
• Lifelong Learning: Keeping pace with evolving technologies and methods.

Example: "Collaborated with cross-functional teams: Delivered end-to-end ML solutions, enhancing product recommendation algorithms and driving a 10% revenue increase."

Education

List your degree(s), relevant coursework, and certifications in machine learning.

For recent graduates, you can include additional details like relevant projects or internships, and you can place this section just below the summary. 

For those transitioning from other fields, condensing the education section to emphasize only the qualifications most applicable to machine learning may be beneficial.

Resume Customization

Tailor your resume for every application.

Fine-tune it to reflect the nuances of the position and the company’s culture to demonstrate that you’ve thoroughly considered your suitability for the role.

Rework your summary to align your skills, experience, and qualifications to the role.

Map key skills, technologies, and qualifications across your Summary, Work Experience, and Skills sections to better understand why you’re adding value to the organization in your desired role.

Additional Sections

Enhance your resume by including additional sections, including:

• Publications: Highlight your research contributions.
• Presentations: Show your ability to communicate complex topics.
• Awards: Demonstrate industry recognition.
• Professional affiliations: Indicate active involvement in the machine learning community.
• Blog posts: Signal your active interest and participation in the ongoing dialogue about current trends or discussions relevant to machine learning. 

Interview Prep

Naturally, getting your foot in the door with an excellent ML engineer resume is the first step.

You'll need to ace the interview to finally get the job offer. Check out some of our interview prep and ML-specific resources to help you do that!

• 💬 Review commonly asked ML engineer interview questions.
• 📖 Read through our company-specific Machine Learning Engineer Interview Guides.
• 👯‍♂️ Practice your behavioral and leadership skills with our mock interview practice tool.
• 👨‍🎓 Take our complete Machine Learning interview course.

FAQs

How long should an ML engineer's resume be?

It depends on your work experience. Usually, an ML engineer's resume should only be one page. You can expand your resume to two pages if you have over five years of experience.

Still, even experienced professionals should reduce their work experience or education sections to only the most relevant details.

What are the key elements of an ML engineer resume?

An ML engineer resume should feature your contact information, a summary, relevant work experience, projects, key skills, and educational background.

How should I format my ML engineer resume?

Maintain a neat and professional design. A single column is preferred. Use easy-to-read fonts, incorporate sufficient white space for clarity, and use bullet points for better legibility.

What additional sections can enhance my ML engineer resume?

Enrich your resume with sections for publications, presentations, awards, professional affiliations, and blog posts.