Machine Learning Engineer resume example
By Bhagirath Sheela, founder of JobCatch ·
Machine learning engineers sit between data science and software engineering, and the resume has to show both: models trained well and systems that serve them reliably. Hiring managers look for production — latency, monitoring, retraining — far more than notebook accuracy.
What recruiters look for
- Models in production: serving latency, throughput, monitoring
- Training pipelines, feature stores and retraining
- Python, PyTorch or TensorFlow, and software engineering habits
- Cloud ML platforms and MLOps tools
Section order
With experience
- Summary
- Skills
- Experience
- Projects
- Education
- Certifications
As a fresher
- Summary
- Education
- Skills
- Projects
- Internships
- Certifications
Summary examples
Fresher
Machine learning engineer with a B.Tech in computer science, an internship deploying a recommendation model, and a published paper on low-resource speech recognition.
Experienced
Machine learning engineer with four years building recommendation and ranking systems. Serves 12 models at under 40 ms p99 to 50 lakh daily users.
Skills, spelled the way postings spell them
An applicant tracking system matches the words in the job posting, and it does not reliably treat two spellings as one skill. The left column is the form most postings use; the right, other spellings people type. Use the posting's own spelling for each application, and list only what you can discuss in an interview.
| Skill | Also written as |
|---|---|
| Python | Python 3 |
| Machine learning | ML |
| Deep learning | — |
| PyTorch | Py Torch |
| TensorFlow | Tensor Flow |
| Scikit-learn | sklearn, Scikit learn |
| Docker | Containerisation, Containerization |
| Kubernetes | K8s, EKS, AKS |
| AWS | Amazon Web Services |
| SQL | Structured Query Language |
| Apache Spark | Spark, PySpark |
| CI/CD | Continuous Integration, Continuous Delivery, CICD |
Also worth listing if you have them: MLflow, Model serving.
Bullet examples
Each bullet says what you did and what changed because of it. Change the numbers to your own; a recruiter will ask about every one.
- Built and run the ranking service that serves 12 models at under 40 ms p99 to 50 lakh daily users.
- Moved training to a scheduled pipeline with MLflow tracking; retraining went from monthly and manual to weekly and automatic.
- Raised click-through on the home feed by 14% with a two-tower retrieval model.
- Cut GPU inference cost by 45% with quantisation and request batching.
- Built drift monitoring that alerts when input features move, catching two silent model failures.
For freshers: projects and internships
- Internship: deployed a product-recommendation model as a FastAPI service in Docker.
- Co-authored a workshop paper on speech recognition for low-resource Indian languages.
- Fine-tuned an image classifier on 20,000 crop-disease photos, reaching 94% accuracy on held-out data.
Mistakes to avoid
- Writing a data scientist's resume. ML engineer roles want serving, scaling and reliability in your bullets.
- No latency, cost or scale figures. Those are what an ML system is judged on in production.
- Listing frameworks without code quality. Tests, CI and code reviews matter as much here as in any engineering role.
What an ATS reads from this sample
An applicant tracking system turns the PDF into plain text before anyone reads it. This is that text for the sample above, extracted by the same code as "What the ATS reads" in the app. Everything is in reading order, under the right heading, with the name and email intact — which is what a single-column template is for.
Tanvi Deshmukh Machine Learning Engineer [email protected] | +91 98765 43210 | Pune, Maharashtra | linkedin.com/in/tanvideshmukh Summary Machine learning engineer with four years building recommendation and ranking systems. Serves 12 models at under 40 ms p99 to 50 lakh daily users. Technical skills Python · Machine learning · Deep learning · PyTorch · TensorFlow · Scikit-learn · Docker · Kubernetes · AWS · SQL · Apache Spark · CI/CD · MLflow · Model serving Projects Drift monitor Feature-drift alerts for production models; caught two silent failures. Experience Machine Learning Engineer, Streamcast Media Feb 2022 – Present Pune Built and run the ranking service that serves 12 models at under 40 ms p99 to 50 lakh daily users. Moved training to a scheduled pipeline with MLflow tracking; retraining went from monthly and manual to weekly and automatic. Raised click-through on the home feed by 14% with a two-tower retrieval model. Software Engineer, ML, Shopverse Commerce Jul 2020 – Jan 2022 Pune Cut GPU inference cost by 45% with quantisation and request batching. Built drift monitoring that alerts when input features move, catching two silent model failures. Education B.Tech, Computer Science 2016 – 2020 Indian Institute of Technology Bombay · 8.9 CGPA Languages English, Hindi
Interview questions to prepare
The app's interview prep for this sample and a plain Machine Learning Engineer posting. It works from the resume and the posting only, and tells you how to prepare, never what to say.
Questions every interview has
- Tell me about yourself. Two minutes: what you do now (Machine Learning Engineer at Streamcast Media), one result you are proud of, and why this role is next.
- Why do you want this job? Read their website and one recent piece of news before the call. Name something specific.
- Why are you leaving your current job? Say what you are moving towards. Do not criticise your employer.
- What is your notice period, and what salary are you expecting? Decide your number before the interview, so you are not deciding it on the call.
About the skills the job asks for
- Tell me about a time you used Python. Pick one real example: what the problem was, what you did, and what changed.
- Tell me about a time you used Machine learning. Pick one real example: what the problem was, what you did, and what changed.
- Tell me about a time you used Deep learning. Pick one real example: what the problem was, what you did, and what changed.
- Tell me about a time you used TensorFlow. Pick one real example: what the problem was, what you did, and what changed.
- Tell me about a time you used PyTorch. Pick one real example: what the problem was, what you did, and what changed.
- Tell me about a time you used Scikit-learn. Pick one real example: what the problem was, what you did, and what changed.
About what the resume claims
- You wrote: "Built and run the ranking service that serves 12 models at under 40 ms p99 to 50 lakh daily users." Expect three follow-ups: how exactly, what was your own part, and how you know it worked.
- You wrote: "Moved training to a scheduled pipeline with MLflow tracking; retraining went from monthly and manual to weekly and automatic." Expect three follow-ups: how exactly, what was your own part, and how you know it worked.
- You wrote: "Raised click-through on the home feed by 14% with a two-tower retrieval model." Expect three follow-ups: how exactly, what was your own part, and how you know it worked.
Questions to ask them
- What would a good first three months in this role look like?
- Who would I work with most closely?
- What are the next steps, and when should I expect to hear back?
Templates that suit this role
All of these are single-column and read cleanly by an applicant tracking system. Classic is free, with no watermark.
Questions
What is the difference between a data scientist and a machine learning engineer?
Data scientists focus on finding what to model and measuring it; ML engineers make models run in production. Many roles overlap, so read each posting and lead with the side it stresses.
Do ML engineer roles need research papers?
No. Papers help for research-heavy teams, but most product companies hire on shipped systems. One deployed model is worth more than a list of courses.
Next: the Machine Learning Engineer cover letter the app drafts from this resume, and how to show Python, Docker, Kubernetes on a resume.
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