Machine Learning Engineer Resume Example

# Priya Sharma
**Machine Learning Engineer** | San Francisco, CA | priya.sharma@email.com | (555) 567-8901

[linkedin.com/in/priyasharma](https://linkedin.com/in/priyasharma) | [github.com/priyasharma](https://github.com/priyasharma)

## Summary

Machine learning engineer with 5 years of experience building and deploying production ML systems. Expertise in recommendation systems, NLP, and LLM applications. Focus on scalable training infrastructure, model optimization, and bridging research to production.

## Experience

### Senior ML Engineer
**OpenAI** | San Francisco, CA | Jan 2023 - Present

- Built and optimized training pipeline for LLM fine-tuning, reducing training time by 40% through distributed training and mixed precision
- Deployed RAG system serving 10M+ queries/day with sub-200ms P99 latency
- Designed evaluation framework for LLM outputs, improving safety metrics by 25%
- Led migration to vLLM for model serving, reducing inference costs by 55%
- Mentored 4 ML engineers on production ML best practices and system design

### ML Engineer
**Airbnb** | San Francisco, CA | Apr 2020 - Dec 2022

- Built recommendation model (0.89 NDCG@10) powering search ranking for 4M+ listings, increasing booking rate by 8%
- Designed and maintained feature store serving 200+ features to 15 ML models in production
- Implemented online A/B testing framework for ML models, running 20+ experiments per quarter
- Reduced model training costs by 60% through GPU optimization and spot instance orchestration

### Data Scientist
**Uber** | San Francisco, CA | Aug 2018 - Mar 2020

- Built surge pricing prediction model improving supply-demand matching accuracy by 15%
- Developed anomaly detection system for fraud prevention, flagging $2M+ in suspicious activity
- Created automated model monitoring pipeline catching data drift and performance degradation

## Skills

**ML/AI:** PyTorch, TensorFlow, JAX, Hugging Face, scikit-learn, XGBoost
**LLMs:** Fine-tuning, RAG, Prompt Engineering, vLLM, LangChain, RLHF
**MLOps:** MLflow, Kubeflow, Ray, Feature Stores, A/B Testing, Model Monitoring
**Infrastructure:** Python, Spark, Kubernetes, AWS (SageMaker, Bedrock), GPU Clusters

## Education

### M.S. Computer Science (ML Specialization)
**Stanford University** | 2016 - 2018
- Research: Published 2 papers at NeurIPS on efficient transformer architectures

### B.Tech Computer Science
**IIT Bombay** | 2012 - 2016