Resume Example
Machine Learning Engineer Resume Example
Show production ML pipelines, inference performance, and monitoring. This sample highlights reliable model delivery.
Modern Minimal
Clean sidebar layout with navy accent. Great for tech and finance roles.
Keywords
Sample bullets
- Productionized a ranking model with sub-80ms inference latency and 99.9% uptime.
- Built training pipelines with MLflow that cut retrain time by 60%.
- Implemented drift monitoring that prevented two major quality regressions.
Soft skills
- Production mindset
- Collaboration with data science teams
- Problem solving
- Documentation
Certifications
- AWS Certified Machine Learning Specialty
- Google Cloud Professional ML Engineer
- TensorFlow Developer Certificate
Why this works
- Focuses on production-grade ML delivery, not just modeling.
- Highlights latency, reliability, and retraining cadence.
- Demonstrates monitoring for drift and quality regressions.
Step-by-Step Guide
How to Write a Machine Learning Engineer Resume
Lead with ML systems in production
ML engineers build production systems. Lead with models deployed, scale served, and business impact. 'ML Engineer building recommendation system serving 50M users with $100M attributed revenue.'
Show end-to-end ML pipeline experience
Include experience across the ML lifecycle: data pipelines, feature engineering, model training, evaluation, deployment, and monitoring. Production MLOps distinguishes ML engineers from data scientists.
Highlight model performance and improvement
Include model metrics (accuracy, AUC, latency), A/B test results, and iteration improvements. 'Improved click-through rate prediction accuracy 15% through feature engineering' shows impact.
Include infrastructure and scale
Describe ML infrastructure: training clusters, serving systems, feature stores, and model registries. Include experience with distributed training and real-time inference at scale.
Demonstrate ML frameworks and tools
List frameworks (TensorFlow, PyTorch, scikit-learn), ML platforms (SageMaker, Vertex AI), and MLOps tools (MLflow, Kubeflow). Include programming languages and cloud experience.
Summary Examples
Good vs. Bad Resume Summaries
“ML Engineer building production recommendation and search systems for e-commerce platform with 20M users. Models drive 35% of revenue. Built real-time inference system handling 10K requests/second with p99 latency under 50ms.”
Production scale, revenue impact, and performance metrics at scale.
“Machine learning engineer experienced with Python and TensorFlow. Strong knowledge of ML algorithms and deep learning.”
No production systems, no scale, no business impact.
“Senior ML Engineer specializing in NLP and LLMs. Built customer service automation handling 1M conversations monthly with 85% resolution rate. Reduced model training time 60% through distributed training optimization.”
NLP/LLM specialty, production scale, business outcome, and infrastructure improvement.
“ML enthusiast with Kaggle experience and personal projects. Passionate about applying ML to real-world problems.”
Kaggle and personal projects don't demonstrate production experience.
Action Verbs
Power Words for Machine Learning Engineer Resumes
Common Mistakes
What to Avoid
- ✗Focusing on algorithms without production experience
- ✗Not quantifying model impact and business outcomes
- ✗Omitting MLOps and deployment experience
- ✗Being vague about scale and performance
- ✗Not showing end-to-end pipeline ownership
- ✗Missing infrastructure and real-time serving experience
Salary ranges
| Level | US | EU | Canada |
|---|---|---|---|
| Entry | USD 96,000-135,000 | EUR 65,000-95,000 | CAD 136,000-149,000 |
| Mid | USD 140,000-200,000 | EUR 70,000-100,000 | CAD 145,000-156,000 |
| Senior | USD 200,000-500,000+ | EUR 75,000-150,000+ | CAD 150,000-200,000 |
Market themes
- LLM integration and RAG pipelines are rising
- Fine-tuning skills (LoRA, PEFT, QLoRA) stand out
US hot markets
- San Francisco
- Seattle
- New York
- Boston
- Los Angeles
EU hot markets
- London
- Berlin
- Munich
- Amsterdam
Canada hot markets
- Toronto
- Montreal
- Vancouver
FAQ
Common questions about this role
What makes ML engineer resumes stand out?
Proof of production ML delivery, latency improvements, and monitoring discipline.
Which metrics matter most?
Inference latency, model lift, and production reliability are key.
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