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?

How candidates describe it

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