The opportunity
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
What you'll do
Develop and deploy ML models across multiple problem domains: including dynamic pricing, marketplace optimization, fraud detection, and anomaly/behavior detection — in production environments serving millions of rides
Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents): that automate decision-making and reduce operational overhead
Design and implement feature pipelines, model training workflows, and serving: infrastructure using Lyft's ML platform
Partner with Data Scientists on the Algorithms and Decisions teams to take: research prototypes from proof-of-concept to production at scale
Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement
Identify new opportunities where ML can create leverage across Lyft Business: verticals (Healthcare, Lyft Pass, Business Travel) and pitch solutions
What they're looking for
- Contribute to team engineering standards: code quality, observability, documentation, and testing practices
- Experience with GenAI / LLM ecosystems: prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning
- Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis)
- Experience with pricing, marketplace, or fraud-related ML problems
