Lyft
Senior
Architect the AI-driven claims management platform inside Lyft's Risk org
Interview for a Senior Software Engineer role on Lyft's Risk Tech team building a Unified Risk Platform for insurance claims management. Expect a mix of distributed-systems architecture depth and applied-AI questions (RAG, agentic workflows, evals, LLM fine-tuning) grounded in a real financial and regulatory domain.
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What this interview tests
- Distributed systems architecture and reliability tradeoffs for a financial/regulatory platform
- Applied AI: multimodal LLMs, RAG pipelines, agentic workflows in production
- AI evaluation, agent tracing, and fine-tuning to improve feature performance
- Claims/insurance domain workflow understanding and bottleneck identification
- Cross-functional leadership across Data Science, Claim Operations, and external insurance vendors
- AI safety/alignment awareness in a high-stakes financial system
Common question themes
Design a system to manage insurance claims workflows at scale with high availability and auditability requirements
Tell me about an AI feature you shipped using RAG or an agentic workflow — how did you evaluate whether it actually worked?
How would you identify and resolve a bottleneck in a claim-handling workflow using AI automation versus traditional engineering?
Describe your approach to building evals and agent tracing to catch regressions in an LLM-powered feature
How do you reason about system design tradeoffs when the cost of an error is a mishandled financial claim?
How have you driven alignment across Product, Data Science, and external partners on a technical integration?