ML Engineer resume guide

This guide helps you tailor a ML Engineer resume to a specific job description while keeping it clear, truthful, and ATS-friendly.

What hiring teams look for

• Experimentation and evaluation discipline
• Model deployment and monitoring
• Data pipelines and feature quality
• Business impact and metrics

Strong resume structure

• Header (name, location, links)
• 2–3 line summary aligned to the role
• Skills (grouped, not a keyword dump)
• Experience (impact-first bullets)
• Projects (optional but powerful)
• Education / certifications (as relevant)

Skills section: what to include

• Modeling: scikit-learn / PyTorch / TensorFlow (primary)
• MLOps: deployment, monitoring, feature stores (if used)
• Data: SQL + pipeline tooling
• Evaluation: AUC, precision/recall, calibration, offline/online

Bullet writing: the formula that works

Use: Action + Method + Result (+ Scope)

Examples:

• “Built an inference service and monitoring, reducing model drift incidents and improving stability.”
• “Improved ranking model metrics (AUC +0.03) and increased conversion by 2%.”
• “Designed experiments and validation pipeline that reduced false positives by 18%.”

ATS and formatting notes

• Be precise with metrics and baselines
• Separate research from production ownership

Common pitfalls

• Listing papers without product impact
• No mention of deployment/monitoring
• Overclaiming model improvements without context

Using HyperApply for this role

• Use HyperApply to match the job’s ML focus (NLP, recommender systems, forecasting) in your summary and bullets.
• Related: /learn/how-to-write-star-bullets-for-your-resume