About the Job
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.
The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable whi...
Agentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, a...
Key Responsibilities
Conduct literature review and build post-training framework and lifecycle. Curate and process human and synthetic data for SFT/LoRA/RLHF/RLAIF/RLVR, and iterate on model quality for real support and safety tasks.
Develop, evaluate, and productionize AI agents, designing tools, state, and control flow in LangGraph (or equivalent) and taking them through the full agent development lifecycle.
Build and scale evaluation frameworks, golden sets, rubric-based grading, LLM-as-judge where appropriate, and regression testing.
Ship models and agents into real-time production, with the monitoring and guardrails needed to operate them safely at millions of interactions a month.
Apply traditional ML (classification, ranking, gradient-boosted trees) where it's the right tool, and partner with product, ops, and data science to scope problems and define success metrics.
3+ years of industry experience in applied ML/AI, inclusive of an MS or PhD in Computer Science, Machine Learning, Artificial Intelligence or a related technical field.
Post-training experience with open-source models. Hands-on familiarity with fine-tuning and preference-tuning paradigms such as SFT, LoRA, RLHF, RLAIF, and RLVR.
Agentic development experience. Built and shipped agents with LangGraph or equivalent frameworks, and comfort with the full agent development lifecycle.
Experience with AI/LLM evaluation. Designed metrics and built offline/online evaluation for generative systems.
Experience deploying ML/AI applications to real-time production use cases.
Strong programming skills in Python and hands-on experience with PyTorch.
Preferred:
Experience applying ML/AI to customer support or trust & safety workflows — agent assist, routing, resolution recommendation, or abuse/safety detection.
PhD in Computer Science, Machine Learning, Statistics, or a related technical field.
Publications at top-tier peer-reviewed research venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).
