Resume example

ML Engineer resume example.

Machine learning engineer roles sit between research and software engineering, and the strongest resumes prove both halves: you can build a model that performs, and you can ship it into production where it actually runs. This example follows an engineer with a master's degree and about four years of experience, with bullets split between model performance and deployment engineering.

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What reviewers look for

  • Model performance numbers (accuracy, latency, AUC) alongside production deployment details
  • Evidence you've shipped a model into a real system, not just a notebook — serving, monitoring, retraining
  • A named ML stack (PyTorch, TensorFlow, scikit-learn) plus MLOps tools if you have them
  • Relevant education (MS or PhD) is common but not required — strong shipped projects can substitute

Keywords to include

PythonPyTorchTensorFlowscikit-learnMachine LearningModel DeploymentMLOpsSQLAWS SageMakerA/B Testing

Pair model metrics with production metrics

"92% accuracy" is only half the story — add what happened after deployment, like latency or business impact.

Prove you ship, not just prototype

A line about serving infrastructure or monitoring separates an ML engineer from a research-only profile.

List your ML stack by name

PyTorch vs. TensorFlow vs. scikit-learn are meaningfully different signals to a hiring manager — name what you actually used.

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