Machine Learning Engineer Resume Examples (2026 Guide)
Also searched as: machine learning engineer resume · ML engineer resume · MLE resume
A machine learning engineer resume is read for models in production: what you shipped, the scale it serves, how it is deployed and monitored, and the business result an experiment measured. Frameworks and cloud tools matter, but only next to what you built with them.
The example below is a complete resume for an ML engineer working on search and recommendations. It states request volume and latency, the A/B test result, and the MLOps platform behind the models, with skills grouped into ML methods, frameworks, MLOps, data pipelines and cloud.
Machine Learning Engineer resume example
A complete sample in the Modern template FitMyRole exports. The person and employers are fictional; the structure, skills and bullet style are what to copy.
Beatriz Herrera
[email protected] • (555) 018-3359 • Seattle, WA • linkedin.com/in/beatrizherrera
Professional Summary
Machine Learning Engineer with 8 years of experience taking models from notebook to production, most recently in search ranking and recommendations for a home goods marketplace with 9 million monthly shoppers. Owns the ranking models behind on-site search and the feature pipelines that feed them, serving about 2,400 requests per second at p95 latency under 45 ms. Shipped a learning-to-rank model and a two-tower PyTorch retrieval model that together raised search conversion 6.8% in an A/B test, and built the MLOps platform the team now uses to train, deploy and monitor every model. Writes production Python, agrees offline and online metrics with product before each launch, and stays on call for the models after they ship.
Technical Skills
- Machine Learning: Learning to rank, recommendation systems, two-tower retrieval, gradient boosting, anomaly detection, embeddings, model evaluation, feature engineering
- Frameworks & Libraries: PyTorch, TensorFlow, XGBoost, LightGBM, scikit-learn, Hugging Face Transformers, FAISS, ONNX
- Languages: Python, SQL, Scala, Bash
- MLOps & Model Deployment: MLflow, Kubeflow Pipelines, Amazon SageMaker, Triton Inference Server, Docker, Kubernetes, GitHub Actions, model monitoring
- Data & Feature Pipelines: Apache Spark, Databricks, Apache Airflow, Feast feature store, Apache Kafka, Snowflake, dbt
- Experimentation & Evaluation: A/B testing, offline evaluation (NDCG, AUC, precision at k), power analysis, drift detection, error analysis
- Cloud & Observability: AWS (S3, EC2, EKS, SageMaker), Terraform, Datadog, Great Expectations
Education & Certification
- Coursework focus: Machine Learning, Statistical Inference, Data Management and Algorithms; capstone built a product recommendation model for a campus bookstore.
- Covers data preparation, model training and tuning, deployment and monitoring on SageMaker.
Work Experience
- Raised search conversion 6.8% in a four-week A/B test by replacing a rules-based ranker with a LightGBM learning-to-rank model trained on 18 months of click and purchase logs.
- Cut zero-result searches from 9% to 2.5% by shipping a two-tower PyTorch retrieval model with FAISS nearest neighbor search alongside the existing keyword match.
- Serve ranking models at about 2,400 requests per second with p95 latency under 45 ms by exporting to ONNX and running them on Triton Inference Server on Kubernetes.
- Built the team’s MLOps platform on Kubeflow Pipelines and MLflow, taking a model from merged code to shadow deployment in under a day instead of two weeks.
- Engineered 140 query, product and shopper features in a Feast feature store backed by Spark jobs on Databricks, removing a training-serving skew that had cost 3% NDCG online.
- Added drift and data quality monitoring to every production model in Datadog, catching a broken catalog feed within an hour instead of the four days it took the previous time.
- Agree launch metrics with product managers before each experiment, pairing offline NDCG targets with online conversion and revenue guardrails reviewed in a weekly experiment forum.
- Mentor two junior engineers and a data science intern, and wrote the model review checklist every model now passes before it reaches production.
- Reduced card fraud losses by $4.1M a year by replacing a logistic regression model with an XGBoost model on 220 engineered transaction and device features, at the same false positive rate.
- Moved fraud scoring from a nightly batch to real-time decisions under 80 ms by deploying the model as a SageMaker endpoint behind the authorization service with the platform team.
- Built velocity and merchant-history features with Spark Structured Streaming on Kafka, which gave the new model its largest single source of lift.
- Cut false declines on legitimate cardholders by 22% by adding a TensorFlow autoencoder anomaly score and recalibrating thresholds by merchant category.
- Documented a model risk review for each release to meet the bank partner’s model governance standard, passing two annual validations without findings.
- Improved delivery-time estimates by 31%, cutting mean absolute error from 9.4 to 6.5 hours, by building a gradient boosting model on route, weather and dock data to replace static lane averages.
- Built Airflow pipelines that loaded telematics data from 1,600 trucks into Snowflake every day, replacing manual CSV exports by the operations team.
- Forecast weekly shipment volume by terminal with scikit-learn models that 14 terminal managers used to plan dock staffing.
- Presented model results and error analysis to operations leaders each month, turning feature importance findings into two changes to dispatch rules.
Want yours to read like this? Upload your resume and a job posting, and FitMyRole rewrites it for that job in this template.
Tailor my resumeWhy This Resume Works
- The summary leads with production, not research: "taking models from notebook to production", 2,400 requests per second and p95 latency under 45 ms are the details that separate a machine learning engineer from a data scientist.
- Model impact is tied to business results a hiring manager can weigh: search conversion up 6.8% in an A/B test, zero-result searches from 9% to 2.5%, and $4.1M a year in fraud losses avoided.
- MLOps is evidenced, not just listed: a Kubeflow and MLflow platform that takes a model from merged code to shadow deployment in under a day, and drift monitoring that caught a broken feed within an hour.
- Feature engineering is shown with scale and consequence, from 140 features in a Feast feature store to the training-serving skew that had cost 3% NDCG online.
- PyTorch and TensorFlow each appear in a bullet that says what was built with them, so an ATS keyword match on either framework is backed by real production use.
Common Resume Mistakes
- Focusing only on research without production or business impact.
- Missing framework or MLOps keywords.
- No metrics (accuracy, latency, adoption).
Keywords Recruiters Look For
- Machine learning
- Python
- TensorFlow
- PyTorch
- MLOps
- Model
- Production
- Feature engineering
Machine Learning Engineer resume FAQ
What should a machine learning engineer resume include?
Models you have put into production and what they did, the scale they serve (requests, latency, users), your frameworks (PyTorch, TensorFlow, scikit-learn), MLOps tooling for training, deployment and monitoring, and measured results from experiments.
How is an ML engineer resume different from a data scientist resume?
An ML engineer resume leads with production engineering: serving, pipelines, latency, reliability and deployment. A data scientist resume leads with analysis, modelling choices and experiments. Many people do both, so lead with whichever the posting asks for.
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