Writing a strong machine learning engineer CV is essential if you want to land interviews for competitive roles. Your CV should highlight your technical depth, real project impact and ability to collaborate across teams. This guide will help you create a machine learning engineer CV that stands out to employers with proven examples, expert advice and ready‑to‑use templates.

Here’s what you’ll find on this page:

  • Machine learning engineer CV examples.
  • Professional CV templates for machine learning roles you can customise quickly to match each job. 
  • A step‑by‑step machine learning CV writing guide. 
  • Dos and don’ts for your machine learning engineer CV.

SEARCH ALL CV EXAMPLES

⚲

    Machine learning CV example 

    Machine Learning CV sample

    Kevin Knight
    London
    07912 345 678
    kevin.knight@example.co.uk

    PROFESSIONAL SUMMARY

    Machine learning engineer with 5+ years of experience building and deploying production AI in finance and e‑commerce. Strong track record of reducing risk, improving customer experience and shipping models that scale. Comfortable owning the end‑to‑end lifecycle from data pipelines to MLOps. Collaborative, pragmatic and focused on measurable outcomes.

    WORK HISTORY

    Machine learning engineer
    Revolut, London, UK
    Jan 2021 to present

    • Built a real‑time fraud detection pipeline using gradient boosting and feature store streaming. Reduced false positives by 27% and avoided an estimated £3.5m in annual losses.
    • Designed credit‑risk scoring on AWS SageMaker with model monitoring and bias checks. Cut approval decision time by 40% while maintaining risk tolerance.
    • Led migration of batch recommenders to online inference on GCP Vertex AI. Increased click‑through on card offers by 18% and lifted monthly active usage by 7%.
    • Introduced MLflow and a standard experiment protocol. Reduced model handover time from 10 days to 3 days and improved reproducibility across teams.
    • Mentored two junior engineers through to promotion, establishing code review and testing practices for model code.

    Data scientist
    ASOS, London, UK
    Jul 2018 to Dec 2020

    • Delivered NLP models for product categorisation that cut manual tagging time by 60% and improved catalogue accuracy by 22%.
    • Built a fashion trend forecasting model with LSTM sequences that improved buying forecasts by 22% and reduced overstock in two categories by 9%.
    • Partnered with search and merchandising to test ranking changes. A/B framework enhancements led to a £12m annualised revenue uplift.

    SKILLS

    • Programming: Python, R, C++
    • ML frameworks: TensorFlow, PyTorch, Scikit‑learn, Keras
    • Data: SQL, BigQuery, Spark, Hadoop, Pandas, dbt
    • Cloud: AWS SageMaker, GCP Vertex AI, Azure ML
    • Techniques: NLP, deep learning, computer vision, recommendation systems, gradient boosting
    • MLOps: Docker, Kubernetes, CI/CD, MLflow, model monitoring, feature stores, REST APIs

    EDUCATION

    MSc Artificial Intelligence
    University College London (UCL), 2015 to 2016BSc Computer Science
    University of Manchester, 2012 to 2015

    Create this CV

    Machine learning engineer CV templates

    Choose from a selection of professional machine learning engineer CV templates designed to showcase your technical expertise and project achievements. Whether you’re applying for an entry-level role or a senior ML position, these templates feature clean layouts, clear structure, and recruiter-approved formatting. Pick the style that best fits your background.

    Choose template

    The best CV format for a machine learning engineer

    When applying for a machine learning role, it’s important to present your technical skills, projects, and career achievements in a clear, professional format. Your CV should immediately show employers that you can design models, handle data at scale, and deliver results in real-world environments.

    For most candidates, the reverse-chronological CV format is the best option. This layout lists your most recent experience first, making it easy to highlight your career progression, project outcomes, and current responsibilities. Since many machine learning engineers have several years of experience, this format demonstrates a solid and relevant career history.

    Machine learning CV formatting tips

    • Keep it concise: Aim for one to two pages, focusing on your most relevant projects, skills, and results.
    • Use a clean, professional font: Stick to fonts like Calibri, Arial, or Helvetica for easy reading.
    • Organise content with clear headings: Include sections for contact details, personal profile, key skills, work experience, projects, education, and certifications.
    • Show achievements with bullet points: Use results-driven examples like “improved model accuracy by 18% using PyTorch” or “reduced cloud costs by 22% through pipeline optimisation.”
    • Save your CV as a PDF: This keeps your formatting consistent and ensures a professional finish.

    What about a skills-based CV?

    A skills-based CV (also known as a functional CV) focuses on grouping your abilities and technical knowledge under key categories, such as machine learning frameworks, data engineering, or cloud platforms, rather than listing jobs in date order.

    This format differs from the chronological CV because it shifts the emphasis from when and where you gained your experience to what you can actually do.

    A skills-based CV works best for:

    • Career changers moving into machine learning from another field (e.g. software engineering, mathematics).
    • Graduates or entry-level candidates with strong projects, internships, or academic work but limited employment history.
    • Professionals with gaps in employment history, where showcasing skills over dates provides a stronger impression.

    However, for most experienced machine learning engineers, a reverse-chronological format remains the preferred option — it gives recruiters a clear picture of progression, real-world impact, and recent projects.

    Choose your format now

    How to write a machine learning engineer CV step by step (With examples)

    Creating a machine learning engineer CV is more than listing job titles and tools. It is your chance to show clear impact, thoughtful problem-solving, and the value your models delivered. Keep the tone confident, specific and easy to scan to help recruiters move you to interview.

    Present your contact details clearly

    Your contact details should sit right at the top of your CV, making it easy for recruiters and hiring managers to get in touch. Keep this section simple, professional, and error-free. Avoid including unnecessary personal details like your date of birth, marital status, or full postal address unless specifically requested.

    Here’s how to format your contact details on a machine learning engineer CV:

    • Full name – write your first and last name clearly.
    • Location – include your country or city. If you’re open to relocation, you can note this too.
    • Phone number – use a mobile number you check regularly.
    • Email address – stick to a professional format, ideally based on your name.
    • Professional links – add your LinkedIn, GitHub or portfolio site to showcase projects and code samples. Be sure they’re up-to-date.

    Example

    Leanne Tegg
    5 Browns Road
    Liverpool, L3 9RF
    07912 345 678
    leanne.tegg@example.co.uk

    Write your personal statement

    Your personal statement (CV summary) appears just under your contact details. It’s a short paragraph, usually 3 to 4 sentences, that introduces you as a candidate and highlights why you’re a strong fit for a machine learning role.

    For machine learning engineers, this section should focus on your technical expertise, project achievements, and the business or research value you’ve delivered. Avoid vague phrases like “hardworking” or “results-driven” — instead, use evidence of measurable impact.

    Here’s what to include:

    • Job title and level – e.g. “Machine learning engineer with 5+ years of experience in finance and e-commerce.”
    • Core strengths – e.g. “Skilled in NLP, recommendation systems, and deploying models on AWS and GCP.”
    • Key achievement or outcome – e.g. “Built a fraud detection system that reduced false positives by 27%, saving £3m annually.”
    • Career goal or focus – e.g. “Now seeking a senior ML role where I can lead model deployment at scale and mentor junior engineers.”

    Tip: Tailor your CV statement to the specific role you’re applying for, aligning your skills with the job description and keywords.

    Example personal statement

    Machine learning engineer with 4 years of experience developing NLP models and recommendation systems. Skilled in Python, TensorFlow, and deploying real-time models on cloud platforms. At Revolut, I built a credit scoring model that cut approval time by 40% while maintaining risk accuracy.

    Create my CV

    Show off your machine learning engineer work experience

    The work experience CV section is the most critical part of your machine learning CV. Recruiters want to see proof that you’ve applied your skills to real projects, delivered measurable results, and worked effectively within teams. Simply listing technologies is not enough — your CV should demonstrate how you used them to add value.

    Use a reverse-chronological format, starting with your most recent role and working backwards. For each position, include:

    • Job title
    • Company name and location
    • Dates of employment (month and year)
    • 3–6 bullet points showing your key responsibilities, achievements, and metrics

    When writing bullet points, begin with strong action verbs (e.g. developed, deployed, optimised, implemented) and quantify results wherever possible.

    Example of work experience

    Machine Learning Engineer
    Revolut, London | March 2020 – Present

    • Designed and deployed machine learning models for fraud detection and credit scoring in a high-growth fintech environment.
    • Built a fraud detection model using gradient boosting that reduced false positives by 27%, saving £3.5m annually
    • Deployed a credit scoring model on AWS SageMaker, cutting approval time by 40% while maintaining risk accuracy
    • Improved recommendation systems by shifting from batch scoring to online inference, increasing customer engagement by 18%
    • Introduced MLflow for experiment tracking, reducing model handover time from 10 days to 3 days
    • Mentored two junior engineers, one of whom was promoted to mid-level within 12 months

    This approach shows impact, technical depth, and leadership potential all in a recruiter-friendly format.

    Add key skills to your machine learning engineer CV

    When applying for a machine learning role, employers look for a balance of technical depth, problem-solving ability, and the soft skills that allow you to collaborate and deliver results. Your CV should highlight both your core technical skills and interpersonal strengths to show you can build models that work in real-world business contexts.

    Aim to include eight to ten key skills that match the role you’re targeting. These should reflect your experience with ML frameworks, data handling, deployment, and teamwork.

    Machine learning–specific skills

    • Designing, training, and deploying supervised and unsupervised learning models
    • Proficiency in Python, R, and SQL for model development and data analysis
    • Advanced use of TensorFlow, PyTorch, and Scikit-learn
    • Building and managing data pipelines with Spark, Hadoop, or Airflow
    • Deploying ML solutions on cloud platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML
    • Applying NLP, deep learning, and computer vision to solve practical problems
    • Implementing MLOps practices including CI/CD, containerisation with Docker and Kubernetes, and model monitoring

    General & soft skills

    • Strong problem-solving and analytical thinking
    • Clear communication of technical concepts to non-technical stakeholders
    • Collaboration with cross-functional teams (engineering, product, data science)
    • Adaptability in fast-moving environments with shifting priorities
    • Time management and ability to deliver projects to deadlines
    • Mentoring and knowledge sharing within teams

    List your education and qualifications

    For engineering roles, your education is just as important as your practical experience. Employers often look for candidates with a solid academic foundation in computer science, data science, mathematics, statistics, or artificial intelligence. A strong degree shows you have the theoretical knowledge and problem-solving skills to succeed in a technical role.

    List your qualifications in reverse-chronological order, starting with the most recent. If you’re an experienced engineer, you can keep this section concise by just listing degrees and certifications. If you’re earlier in your career, include relevant modules, research projects, or dissertation topics to highlight your expertise.

    Here’s what to include:

    • Degree title and level – e.g. MSc Machine Learning
    • Institution name – e.g. Imperial College London
    • Location – City and country
    • Dates attended or graduation year

    Example of an education section

    MSc Machine Learning
    Imperial College London, UK
    Graduated: 2020

    BSc Computer Science
    University of Manchester, UK
    Graduated: 2018

    Tip: Strengthen this section by adding professional certifications (such as AWS Certified Machine Learning – Specialty or Google Cloud Professional ML Engineer). They show employers you’re up to date with industry tools and platforms, making your CV even more competitive.

    Dos and don’ts for a machine learning engineer CV

    DO
    DO emphasise your results and impact.

    Recruiters want to see how your work added measurable value — not just the tools you used. Use active language and include metrics. For example, “deployed a fraud detection model that reduced false positives by 27% and saved £3.5m annually” is far stronger than simply writing “built classification models.”

    DO highlight collaboration and knowledge sharing.

    Machine learning roles often involve cross-functional work with engineers, product managers, and data scientists. Show how you’ve worked in teams, mentored juniors, or contributed to open-source projects. This demonstrates you bring value beyond code.

    DON’T
    DON’T rely on buzzwords without evidence.

    Avoid vague claims like “AI expert” or “problem solver” unless you can back them up with context. Instead, use examples such as: “implemented an NLP pipeline using BERT that improved search relevance by 18%.” Concrete outcomes prove your expertise.

    DON’T skip tailoring your CV.

    Sending the same CV to every job is a missed opportunity. Customise your CV to reflect the skills and frameworks listed in each job advert — whether it’s TensorFlow vs PyTorch, AWS vs GCP, or emphasis on MLOps vs research. Tailoring ensures your CV aligns with both ATS systems and recruiter expectations.

    Your machine learning CV questions answered

    How long should a machine learning engineer CV be?

    Aim for one to two pages. Early-career engineers can usually fit their CV on one page, while experienced professionals may need two. Focus on your most relevant experience, tools, and measurable achievements.

    Should I include personal projects or Kaggle competitions?

    Yes. If you’re a graduate, career-changer, or applying for an entry-level role, projects and Kaggle competitions are a great way to demonstrate your practical skills. Include links to GitHub or a portfolio site so recruiters can see your code.

    Do I need a degree to become a machine learning engineer?

    Most roles require at least a bachelor’s degree in computer science, data science, mathematics, or a related field. An MSc or professional certifications (such as AWS Certified Machine Learning or Google Cloud ML Engineer) can make your CV more competitive.

    How do I make my CV stand out to recruiters?

    Use measurable results, tailor your CV to the job description, and keep your formatting clean and professional. Highlight not just what you built but also the impact — for example, “improved recommendation accuracy by 15% leading to higher user engagement.”

    How important is MLOps knowledge for a CV?

    Very important for most production-level roles. Employers want machine learning engineers who can not only build models but also deploy, monitor, and maintain them in real systems. Include any experience with Docker, Kubernetes, CI/CD, or MLflow.

    Do I need a cover letter with my machine learning engineer CV?

    A cover letter isn’t always necessary for a job application — and many employers won’t ask for one. However, including a tailored cover letter can give you an edge over other applicants. It’s your chance to explain why you’re interested in the role, highlight specific achievements, and show how your skills align with the company’s goals. For inspiration, explore our cover letter templates.

    Build my CV

    Create your machine learning engineer CV today

    Showcase your technical skills, projects, and real-world impact with a professional machine learning engineer CV. Use our expert tips and templates to craft an application that stands out to recruiters. Start now with our easy to use CV builder and download your CV in minutes.

    Build my CV

    build-cv

    *The names and logos of the companies referred to above are all trademarks of their respective holders. Unless specifically stated otherwise, such references are not intended to imply any affiliation or association with myperfectCV.

    Need help?