Data · Resume guide
MLE resumes are screened as engineering-first: MLOps tooling, serving infrastructure, and latency/throughput numbers rank above modeling theory, and JDs name deployment stack components (Docker, Kubernetes, feature stores) explicitly.
These terms appear repeatedly in real machine learning engineer job descriptions. Use the exact forms below where your experience honestly supports them — exact matches rank; paraphrases often don't.
Share of 174 live machine learning engineer postings in our 15 August 2026 snapshot . Front-load the ones your target job description actually names.
| Skill | Share of postings |
|---|---|
| Machine Learning | 82% |
| LLM | 60% |
| Python | 59% |
| PyTorch | 37% |
| Deep Learning | 27% |
| TensorFlow | 26% |
| NLP | 20% |
| SQL | 20% |
| AWS | 18% |
| Spark | 18% |
| GCP | 15% |
| CI/CD | 14% |
Public ATS boards (Arbeitnow, Greenhouse, Ashby, Google Careers, Lever, RemoteOK, Remotive). Not LinkedIn or Indeed. Snapshot, not a forecast — always mirror the posting in front of you.
Strong machine learning engineer bullets pair the role's vocabulary with a measured outcome. Use these as patterns — with your real numbers, never invented ones.
“Built the model-serving platform (Kubernetes, ONNX, autoscaling) that cut inference p99 from 400ms to 45ms across 9 production models”
“Automated retraining pipelines with Airflow and drift monitoring, taking model refresh from quarterly manual work to weekly hands-off runs”
Paste the machine learning engineer job description you're targeting and extract its required skills and repeated phrases.
Rephrase your real experience with the posting's exact terminology — same work, their words.
Reorder your summary, skills, and top bullets so this role's priorities lead.
Verify: check your match score and remaining keyword gaps before submitting.
Lead with production-ML vocabulary — serving, monitoring, retraining, drift — and quantify latency/throughput/uptime like a backend engineer would. JDs distinguish MLE from data scientist by exactly these operational keywords.
The highest-weight terms for machine learning engineer postings currently include: Machine Learning, LLM, Python, PyTorch, Deep Learning, TensorFlow. The definitive list is always the specific job description — paste it into a free keyword check to see exactly which terms your resume shows and which it's missing.
Extract the job description's required skills and repeated phrases, then rephrase your real experience using those exact terms — lead your summary and top bullets with what that posting emphasizes. Never add skills you don't have; keep genuine gaps visible and address them honestly.
Upload your resume, paste the job description, and get your free ATS match score with every missing keyword — then a tailored, ATS-safe rewrite without fabrication.