
Staff Engineer - Backend & Data
10h10 hours agoTrial Library, Inc.
San Francisco, US · Full-time · $210,000 – $275,000
About this role
Trial Library is an AI-native enrollment and care navigation platform that accelerates access to precision medicine. The company enables clinical trials as a standard care option through collaboration with biopharmaceutical manufacturers, payers, and health systems. Its platform is currently deployed in 840+ clinics and 3,000+ providers nationwide.
We are hiring a second Staff Engineer to own key parts of the backend and data platform. You will take technical leadership of critical systems during a massive architectural shift from narrow human-assisted data pulls to robust automated EMR integrations. This change will enable ingestion of complete patient records at scale.
Our engineering culture values direct communication, strong ownership, and low-ego collaboration. We make decisions quickly, give open feedback, and maintain a strong bias toward action while using AI tools throughout the SDLC for coding, testing, and iteration. The team laughs a lot and relies heavily on Slack for day-to-day coordination.
The backend is primarily Python and actively moving toward Typescript, with infrastructure on AWS using Lambda, Bedrock, Fargate, SQS, and RDS managed via Terraform. The data layer centers on PostgreSQL and Drizzle. This role offers the chance to pair strong engineering fundamentals with AI tooling to reinvent patient-trial matching workflows.
Requirements
- Strong experience with Python and modern Typescript backend development
- Hands-on work with AWS services including Lambda, Fargate, SQS, RDS, and Bedrock
- Proficiency with PostgreSQL and Drizzle or similar ORM tools
- Experience building and operating cloud-native systems handling sensitive healthcare data
- Track record of leading architectural changes and owning reliability of production workflows
- Comfort integrating AI tooling into the software development lifecycle for coding, testing, and iteration
Responsibilities
- Own key parts of the backend and data platform supporting patient identification and trial navigation workflows
- Take technical leadership of critical systems during the architectural shift to automated EMR integrations
- Design for reliability in compute-intensive, long-running workflows where serial processing breaks under load
- Build AI agents that empower human experts by handling operational tasks such as patient ranking and scheduling
- Explore non-deterministic approaches to patient-trial matching while maintaining HIPAA-grade data standards
- Evolve pragmatic architecture decisions that support developer velocity as both product and AI capabilities mature
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