Machine Learning Engineer Resume Keywords (Technical Skills Keywords + ATS Examples)
MLE technical skills bridge research tooling and production engineering. Reflect both sides explicitly.
Training frameworks & hardware
Modeling stack.
- PyTorch
- TensorFlow
- JAX
- CUDA
- mixed precision
- distributed training
- Horovod
- PyTorch Lightning
- ONNX
- TorchScript
Weak: PyTorch
Strong: Distributed training with gradient accumulation for large batch sizes.
Weak: GPU
Strong: Profiled kernels; moved hot paths to fused ops improving step time.
Feature pipelines & data
Production ML backbone.
- feature store
- Feast
- Airflow
- Spark
- BigQuery
- data validation
- Great Expectations
- point-in-time correctness
- backfills
- streaming joins
Weak: Feature store
Strong: Point-in-time joins preventing label leakage in training data.
Weak: Pipelines
Strong: Airflow DAGs with SLA monitors on freshness.
Serving, inference & efficiency
Latency and cost.
- TorchServe
- TensorRT
- ONNX Runtime
- batch inference
- real-time inference
- autoscaling
- GPU sharing
- quantization
- distillation
- caching
Weak: Inference
Strong: Batch inference for nightly scoring with cost controls.
Weak: Latency
Strong: Quantization trade study holding quality within tolerance.
MLOps & experiment tracking
Operational rigor.
- MLflow
- Weights & Biases
- Kubeflow
- model registry
- artifact storage
- CI for ML
- reproducibility
- environment pinning
- data versioning
- pipeline orchestration
Weak: MLflow
Strong: Tracked experiments; promoted only models passing offline gates.
Weak: CI
Strong: Automated training smoke tests on schema change PRs.
Monitoring & responsible ML
Production safety.
- drift detection
- data drift
- model monitoring
- Evidently
- whylogs
- SHAP
- fairness metrics
- bias testing
- shadow deployments
- rollback
Weak: Monitoring
Strong: Alerts on score distribution shifts with automated rollback hooks.
Weak: Explainability
Strong: SHAP summaries for risk model governance.
Where to use these keywords (ATS + readability)
Skills
Split Modeling / Data / Serving / Ops—avoid one undifferentiated list.
Experience bullets
Prove production: latency SLOs, drift incidents, retraining triggers.
Skills
Include feature-store/pipeline terms when JD does.
Summary
Name domain: ads, recommendations, risk, NLP.
Experience bullets
Mention GPU/scale when relevant.
Common mistakes
- Research-only stack without serving or monitoring.
- Listing frameworks without training/serving proof.
- Ignoring data quality and leakage controls.
- No cost/latency trade-off language.