Lyft
Machine Learning Engineer
San Francisco, CA$140.8k–$176kfull-timemidAdded today
About this role
Join Lyft's Fulfillment team as a Machine Learning Engineer to design and deploy cutting-edge ML systems that power ride-matching and inventory management at scale. You'll translate research into production solutions, build feature pipelines, and collaborate across teams to optimize core marketplace operations.
What you'll do
- Design, build, and deploy real-time ML models, bridging state-of-the-art research to production
- Develop feature pipelines, model training workflows, and serving infrastructure on Lyft's ML platform
- Measure ML system performance against business KPIs and run experiments for continuous improvement
- Collaborate with ML engineers, product managers, data scientists, and software engineers on alignment
- Extract data-driven insights to inform ML strategies and refine solutions
- Write production-quality code and participate in code reviews
What they're looking for
- Machine learning modeling and research translation
- Deep learning frameworks (TensorFlow, PyTorch)
- Python and/or Golang programming
- Statistical analysis and hypothesis testing
- Feature engineering and ML pipeline design
- Real-time systems and distributed computing
- Recommendation systems
- Cross-functional communication
Benefits
- Comprehensive medical, dental, and vision insurance
- Mental health benefits
- Family building and parental leave (18 weeks paid)
- 401(k) with company match
- Discretionary PTO and 12 observed holidays
- Subsidized commuter benefits and Lyft credits
Opens the official application on the employer’s site. No login required.
Lyft
Lyft develops micromobility hardware solutions including electric bikes and scooters for urban transportation. The company is hiring firmware test engineers and other technical roles to ensure the quality and sustainability of its products.
- Website
- lyft.com
Likely interview questions
- Walk us through a time you translated a research paper or novel ML technique into a production system—what challenges did you face?
- How do you approach designing feature pipelines and model serving infrastructure for real-time applications?