Machine Learning Engineer ATS Keywords

Top ATS keywords for ML Engineer resumes — the MLOps tools, serving infrastructure, and LLM terms applicant tracking systems screen for.

Essential Keywords

Technical Skills

Machine Learning EngineeringMLOpsModel DeploymentModel ServingDistributed TrainingFeature EngineeringDeep LearningLLM Fine-TuningRetrieval-Augmented Generation (RAG)Model MonitoringPythonCI/CD for ML

Soft Skills

Cross-functional CollaborationTechnical CommunicationProblem SolvingMentoringTrade-off AnalysisDocumentation

Tools & Software

PyTorchTensorFlowHugging FaceMLflowKubernetesDockerSageMakerVertex AIRayTriton Inference ServerFeastAirflow

Certifications

AWS Certified Machine Learning — SpecialtyGoogle Professional Machine Learning EngineerDatabricks Certified ML ProfessionalNVIDIA Deep Learning Institute Certificates

Action Verbs for Machine Learning Engineer Resumes

DeployedProductionizedServedFine-tunedQuantizedScaledOptimizedContainerizedMonitoredAccelerated

Industry Terms

Training-Serving SkewFeature StoreModel RegistryInference Latency (p99)Quantization & DistillationVector DatabaseEmbeddingsLLM EvaluationPrompt EngineeringShadow DeploymentDrift DetectionGPU Optimization

How to Use These Keywords

1

Include LLM-era terms (RAG, fine-tuning, vector database, evals) — they're the fastest-growing recruiter searches in ML hiring.

2

Pair serving keywords with latency/throughput numbers ('35ms p99 at 40M requests/day') to convert ATS matches into interviews.

3

List both 'MLOps' and its components (model registry, feature store, monitoring) — postings filter at both levels.

4

Show framework depth on one of PyTorch or TensorFlow rather than claiming both equally — interviewers probe the first listed.

5

Include 'production' as a literal word — 'production ML' is a top recruiter search phrase distinguishing engineers from researchers.

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