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Machine Learning Engineer resume template.2026

ML engineer hiring in 2026 leans heavily on inference infra and production model ownership. Recruiters want to see shipped models with latency / cost / quality numbers — not just notebooks.

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  • ATS-ready format
  • Pulls from your LinkedIn
  • One page, 30 seconds
Template
Your name

Machine Learning Engineer

Summary

ML engineer with 5 years owning production models end-to-end. Shipped a low-latency ranking model serving 4k QPS at 35ms p95, and built the training infra now used by 12 ML engineers.

Selected wins

  • Shipped a ranking model serving 4k QPS at p95 35ms; lifted CTR +14% in production.
  • Built the training infra (Ray + PyTorch + S3) now used by 12 ML engineers across 4 teams.
  • Cut inference cost 62% by rewriting the serving layer in Rust and adding batching.

Skills

  • Model serving
  • Training infra
  • MLOps
  • Latency tuning
  • Cost optimisation
Summary

Three sentences. Role, win, scope.

"ML engineer with 5 years owning production models end-to-end. Shipped a low-latency ranking model serving 4k QPS at 35ms p95, and built the training infra now used by 12 ML engineers."

Open with the role and years of experience. Name a measurable win. Close with the scope or ownership signal. Recruiters scan the top third for under seven seconds — this is the format that survives that scan.

Bullets

Verb. Number. Outcome.

  • Shipped a ranking model serving 4k QPS at p95 35ms; lifted CTR +14% in production.
  • Built the training infra (Ray + PyTorch + S3) now used by 12 ML engineers across 4 teams.
  • Cut inference cost 62% by rewriting the serving layer in Rust and adding batching.
What to highlight

Skill stack

  • Model serving
  • Training infra
  • MLOps
  • Latency tuning
  • Cost optimisation
Typical 2026 pay
$160k – $340k base in US
For the bots

ATS keywords

  • Python
  • PyTorch
  • TensorFlow
  • Ray
  • MLflow
  • Kubernetes
  • ONNX
  • model serving
  • inference
  • training
  • MLOps
  • feature store
  • Triton

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