Drimva

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.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 5, 2026 (UTC)