Machine Learning Engineer Resume Keywords (Action Verbs + ATS Examples)
MLE bullets should emphasize production: trained, deployed, monitored, reduced latency—not only research.
Training & evaluation verbs
Model work.
- trained
- fine-tuned
- evaluated
- benchmarked
- ablated
- distilled
- quantized
- compressed
- validated
- reproduced
Weak: Built models
Strong: Fine-tuned transformer rankers; improved NDCG@10 6% offline before online shadow.
Weak: Trained
Strong: Ran distributed training with mixed precision reducing wall-clock 35%.
Deployment & serving verbs
Production path.
- deployed
- containerized
- scaled
- autoscaling
- canaried
- rolled back
- load tested
- optimized inference
- reduced latency
- cut costs
Weak: Deployed model
Strong: Served models on GPU autoscaling from queue depth; held p99 under 50ms.
Weak: Inference
Strong: Quantized model reducing GPU cost 25% with offline quality gates.
Data & feature pipeline verbs
MLE systems.
- built pipelines
- engineered features
- validated data
- monitored freshness
- backfilled
- fixed skew
- improved freshness
- reduced leakage
- standardized
- versioned datasets
Weak: Features
Strong: Cut training-serving skew by moving transforms to shared libraries.
Weak: Pipelines
Strong: Improved feature freshness from weekly to daily enabling timely retraining.
Monitoring & reliability verbs
Production ML ops.
- monitored drift
- set alerts
- retrained
- rolled forward
- investigated incidents
- reduced false positives
- improved robustness
- audited
- documented runbooks
- owned on-call
Weak: Monitoring
Strong: Set drift alerts tied to automated rollback on scoring jobs.
Weak: Incidents
Strong: Reduced model-related incidents 40% with retraining triggers and data checks.
Weak MLE phrasing
Notebook vs production.
- used sklearn
- played with models
- research only
- jupyter notebooks
- experimented
- various algorithms
Weak: Research
Strong: Productionized churn model with monitoring, rollback, and weekly retrain cadence.
Weak: ML project
Strong: Owned inference service on Kubernetes with autoscaling and canary releases.
Where to use these keywords (ATS + readability)
Experience bullets
Pair training verbs with datasets, metrics, and constraints.
Experience bullets
Deployment verbs need latency, throughput, or cost numbers.
Summary
Position as ML in production, not only experimentation.
Skills
Libraries in skills; verbs in experience.
Experience bullets
Monitoring verbs need incident or drift examples.
Common mistakes
- Research metrics without online story.
- ‘Deployed’ without latency/cost/quality tradeoffs.
- Missing monitoring/drift language for production roles.
- Algorithm names without evaluation rigor.