Lyft
Senior AI Software Engineer, Risk - Insurance Claims Management
$136K–$170K
Seattle, WA +2 · Senior · Full-time · Posted Sep 14, 2026
- Location
- Seattle, WA
San Francisco, California, United States
Seattle, Washington, United States - Experience
- 6+ years of professional experience building scalable distributed systems.
- Education
- B.S., M.S. in Computer Science or related technical field or relevant work experience.
About the job
Lead technical direction and development of a scalable insurance claims management platform, using AI automation to improve claims workflows.
What you'll do
- Set the claims systems roadmap and guide architecture and design decisions across engineering teams.
- Build reliable, scalable claims platform solutions and integrations with internal teams and external insurance partners.
- Identify claims workflow bottlenecks and improve productivity with AI automation; support reviews, on-call, and incident response.
What they ask for
- 6+ years building scalable distributed systems with high availability, observability, and reliability requirements.
- Experience building end-to-end products or platforms with cross-functional stakeholders and reasoning about system design, architecture, domain modeling, and operational cost.
- Experience developing AI features using multimodal LLMs, RAG pipelines, agentic workflows, or related AI frameworks, and improving them through evaluations, tracing, fine-tuning, optimization, and feedback loops.
- Proficiency in Python; B.S. or M.S. in Computer Science or related technical field, or relevant work experience.
Nice to have
- Knowledge of React/Next.js and data analysis.
- Insurance, claims management, workflow orchestration, external vendor, AI safety/alignment, machine learning, NLP, or A/B testing experience.
Pay
$136,160 - $170,200 USD base salary
Tools
Python, React/Next.js, Spark, Kafka, Airflow, multimodal LLMs, RAG pipelines
AI focus
Develop AI features and automation for claims workflows; improve performance with robust evaluations, agent tracing, LLM fine-tuning, RAG optimization, and feedback loops; use AI tools to enhance development efficiency.
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