ML Engineer Resume Guide
Showcase your machine learning expertise, model development skills, and ability to deploy ML at scale. Build a resume that lands AI/ML roles.
Markus Fink
Senior Technical Recruiter, Ex - Google, Airbnb
What You'll Learn
ML Role Types
ML roles vary significantly. Know which you're targeting:
🔬 Research Scientist
Novel algorithms, publications, theoretical contributions
⚙️ ML Engineer
Production systems, model deployment, MLOps
📊 Applied Scientist
Business problems, product features, A/B testing
🤖 AI Engineer
LLM integration, prompt engineering, AI applications
ML-Specific Metrics
ML resumes need specialized metrics:
- Model performance — 'Improved accuracy from 85% to 94%'
- Business impact — 'Increased CTR by 25% through personalization'
- Scale — 'Model serving 1M predictions/day with <50ms latency'
- Cost — 'Reduced inference cost by 60% through model optimization'
- Data — 'Trained on 10B token dataset'
Always connect model metrics to business outcomes when possible.
Technical Skills
Languages
Python (required), SQL, C++ (for optimization)
ML Frameworks
PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face
MLOps
MLflow, Kubeflow, SageMaker, Vertex AI, Weights & Biases
Data
Spark, Ray, Dask, feature stores
Research vs Industry
Tailor your resume based on the role type:
🔬 Research Positions
- Lead with publications
- Focus on novel contributions
- Include conference talks, citations
💼 Industry Positions
- Lead with production impact
- Emphasize deployment experience
- Show business metrics, not just model metrics
Bullet Point Examples
✅ Strong (Production)
"Deployed recommendation model serving 10M users, increasing engagement by 35% and generating $5M incremental annual revenue."
✅ Strong (Optimization)
"Reduced LLM inference costs by 70% through quantization and batching optimizations while maintaining 95% of original quality."
✅ Strong (Research)
"First-authored paper on efficient transformers accepted at NeurIPS, with open-source implementation achieving 100+ GitHub stars."