Machine Learning Engineer Resume Template

A clean template structured for ML roles — designed to showcase model development, feature engineering, and the infrastructure work that takes machine learning from a Jupyter notebook to a production system serving real users.

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Marcus Thompson

marcus.thompson@email.com | (206) 555-0419 | linkedin.com/in/marcusthompson | github.com/mthompson-ml

Summary

Machine learning engineer with 4 years of experience building and deploying ML models at scale. Built Spotify’s podcast recommendation engine serving 50M+ users, improving listener retention by 12% through a hybrid collaborative filtering and content-based approach with real-time feature serving.

Experience

Senior ML Engineer Aug 2023 – Present

Spotify New York, NY

Designed and deployed a hybrid recommendation model combining collaborative filtering with transformer-based content embeddings, increasing podcast discovery rate by 18% and listener retention by 12% across 50M+ monthly active users
Built a real-time feature store using Apache Flink and Redis that serves 200+ ML features with p99 latency under 15ms, replacing a batch pipeline that had 6-hour data staleness
Developed an A/B testing framework for ML models that reduced experiment cycle time from 3 weeks to 4 days, enabling the team to run 8x more experiments per quarter

ML Engineer Jul 2021 – Jul 2023

Instacart San Francisco, CA

Trained and deployed a demand forecasting model using LightGBM and time-series features that reduced inventory waste by 22% across 800+ retail partner locations
Built an automated model retraining pipeline using Airflow and MLflow that detected data drift and triggered retraining, keeping model accuracy above 91% through seasonal demand shifts
Optimized the product search ranking model by implementing learning-to-rank with LambdaMART, increasing search-to-purchase conversion by 8%

Skills

Languages: Python, Scala, SQL ML: PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, Hugging Face Infrastructure: MLflow, Airflow, Spark, Flink, Kubernetes, AWS SageMaker Data: PostgreSQL, BigQuery, Redis, Delta Lake, Feature Stores

Education

M.S. Machine Learning 2021

Carnegie Mellon University

What makes a strong ML engineer resume

The notebook-to-production gap is what they’re hiring for

Every ML job posting gets flooded with candidates who can train a model in a notebook. What companies actually struggle to find are engineers who can get that model into production and keep it running reliably. Your resume should emphasize the full lifecycle: data pipeline design, feature engineering, model training, deployment, monitoring, and retraining. If your bullets stop at “achieved 94% accuracy on the test set,” you’re missing the point. The hiring manager wants to know: did it ship? How many users does it serve? What happens when the data drifts?

Feature engineering deserves its own bullet points

In most ML systems, the features matter more than the model architecture. If you built a feature store, designed real-time feature pipelines, or figured out which signals actually predicted the outcome — those are some of your strongest resume bullets. A bullet like “engineered 40+ features from raw clickstream data, improving model AUC from 0.78 to 0.86” shows more ML maturity than “fine-tuned a BERT model.”

Use ML-specific metrics, not business-speak

Hiring managers for ML roles want to see AUC, F1 scores, precision/recall tradeoffs, latency, throughput, data volumes, and experiment results. “Improved efficiency by 30%” could mean anything. “Reduced model inference latency from 120ms to 35ms while maintaining AUC above 0.91” tells them exactly what you did and that you understand the tradeoffs. Both technical depth and business impact matter — but lead with the technical specifics.

Infrastructure skills aren’t optional anymore

ML engineering in 2026 is as much about infrastructure as it is about modeling. Listing MLflow, Airflow, Spark, feature stores, model registries, and CI/CD for ML pipelines isn’t optional — it’s expected. If you can train a model but can’t deploy it, you’re a data scientist, not an ML engineer. Make sure your skills section and your bullet points both reflect the infrastructure side of your work.

Key skills for ML engineer resumes

Include the ones you actually have. Leave out the ones you’d struggle to discuss in an interview.

Technical Skills

Python PyTorch TensorFlow scikit-learn XGBoost Spark SQL MLflow Airflow Kubernetes Docker AWS SageMaker Feature Stores Delta Lake

What ML Interviews Focus On

Model Selection Feature Engineering Experiment Design A/B Testing Data Pipelines Model Monitoring Bias Detection Scalable Training Statistical Foundations System Design for ML

Recommended template for ML engineering roles

Classic

For ML engineering roles, the Classic template is the standard. ML hiring often involves both recruiter screens and deep technical interviews, and a clean serif layout ensures your content is easy to parse at both stages. The single-column format also handles the longer, more technical bullet points that ML resumes tend to need.

If you’re applying to startups or design-adjacent teams, the Modern template (with teal accents and a sans-serif font) is a good alternative. But when in doubt, Classic is the one that never raises eyebrows.

Use this template

Frequently asked questions

What’s the difference between a machine learning engineer and a data scientist?

Data scientists focus on analysis, experimentation, and insight generation — often working in notebooks and communicating findings to stakeholders. ML engineers focus on building and deploying models as production systems — writing production code, building data pipelines, and ensuring models run reliably at scale. Your resume should reflect which side you’re on through your bullet points and skills.

Should I list Kaggle competitions on my ML resume?

Only if you placed in the top 5–10% or won a medal. A strong Kaggle result shows you can model effectively under constraints, which is a real skill. But if you’re listing competitions where you placed 500th, it’s better to replace that space with a production ML project. Hiring managers care more about shipped systems than leaderboard rankings.

How technical should my ML resume be?

Very. ML hiring managers expect to see specific model architectures, framework names, data scales, and performance metrics. A bullet that says “built a machine learning model to improve recommendations” is too vague. A bullet that says “trained a two-tower retrieval model on 200M interaction pairs using PyTorch, improving recall@10 from 0.32 to 0.48” tells them you know what you’re doing.