Senior AI Architect for Risk & Claims Platform (Hybrid)

Lyft; Seattle (WA); Hybrid; USD 136,000 - 170,000; Full time

Benefits offered by this job

Health benefits

Parental leave

Commuter benefits

401(k) plan

Job summary

Lyft seeks a Senior AI Software Engineer for Risk – Insurance Claims Management in Seattle on a hybrid schedule, 3 days in-office. The role drives the architecture of a unified risk platform for claims workflows and collaborates with Engineering, Data Science, Product, and Claim Operations to deliver end-to-end AI-enabled solutions.

The candidate will build scalable systems, lead architectural decisions, and work across teams to implement AI features using LLMs, RAG pipelines, and automation

Qualifications

• 6+ years of professional experience building scalable distributed systems.
• Experience with multimodal LLMs, RAG pipelines and AI frameworks.
• Proficiency in Python; knowledge of React/Next.js and data analysis is a plus.
• Experience with data processing frameworks and cloud provisioning environments.

Responsibilities

• Define and drive the long-term technical roadmap for claims management systems.
• Provide architectural leadership and guide design decisions across multiple teams.
• Identify bottlenecks and drive platform innovations with AI automation solutions.
• Deliver scalable, reliable, high quality and well-tested solutions.
• Collaborate with internal teams and external partners for alignment and integrations.

Skills

Python

React/Next.js

Data analysis

Distributed systems

Education

B.S./M.S. in Computer Science or related field

Tools

Spark

Kafka

Airflow

Job description

Lyft seeks a Senior AI Software Engineer for Risk – Insurance Claims Management in Seattle on a hybrid schedule, 3 days in-office. The role drives the architecture of a unified risk platform for claims workflows and collaborates with Engineering, Data Science, Product, and Claim Operations to deliver end-to-end AI-enabled solutions.

The candidate will build scalable systems, lead architectural decisions, and work across teams to implement AI features using LLMs, RAG pipelines, and automation