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San Francisco, California, United States
Remote only
Trial Library is an AI-native enrollment and care navigation platform that accelerates access to precision medicine. In collaboration with biopharmaceutical manufacturers, payers, and health systems, Trial Library enables the delivery of clinical trials as a standard care option - improving patient access, advancing oncology outcomes, and reducing the total cost of care. Backed by leading healthcare venture capital firms, Trial Library’s platform is currently deployed in 840+ clinics and 3,000+ providers nationwide.
We're hiring a second Staff Engineer to own key parts of our backend and data platform. Our platform supports our teams that identify eligible patients for clinical trials and navigate patients around any barriers to enrolling in a trial. You will take technical leadership of our most critical systems as we navigate a massive architectural shift that will be a step-change in our ability to refer patients to trials: moving from narrow, human-assisted data pulls to robust, automated integrations with EMR systems to ingest complete patient records. If cloud-native architecture, building AI agents to empower our human experts, exploring non-deterministic approaches to patient-trial matching, and handling HIPAA-grade data sounds like the right problem space, please keep reading. Our engineering culture values direct communication, strong ownership, and low-ego collaboration. We laugh a lot and use a ton of Slack emojis. We make decisions quickly, give open feedback, and we have a strong bias toward action.
Our use of AI in our products is intended to make our authentic interactions with patients, doctors, and others as meaningful as possible. That means using AI to help with all of the operational aspects of the interactions, from helping decide who to call and when, to assisting with transportation scheduling, to queuing up ranked potentially eligible patients for a human to review and validate. This enables our people to focus 100% on authentic interactions with other people.
AI is also deeply embedded in our SDLC. Our embrace of AI didn't come from a mandate but from our own desire as engineers to do more safely. We use it for coding, testing, measuring, and iterating. We've seen what it unlocks and treat it as a genuine competitive advantage. We view effective use of these tools as an important part of engineering leverage and velocity.
We are looking for someone who shares that instinct: someone excited to reinvent long-standing processes, question assumptions about where human attention should be focused, and think ambitiously about what is possible when you pair strong engineering fundamentals with AI tooling. If you are already building that way, you will fit right in.
Underneath all of this is a modern, evolving platform built for scale and iteration. Our backend is primarily Python and actively moving toward Typescript. Our frontend is a React (Typescript) SPA, and our infrastructure is built on AWS using Lambda, Bedrock, Fargate, SQS, and RDS with Terraform-managed infrastructure. Our data layer is centered around PostgreSQL and Drizzle. We care deeply about pragmatic architecture decisions, developer velocity, and building systems that can evolve quickly as both our product and AI capabilities mature.
You will own the systems that power Trial Library's core workflows and be accountable for their reliability, evolution, and scale.
A meaningful part of that is designing for reliability in compute-intensive, long-running workflows: the kind of problems where serial processing breaks under load, timeouts become production incidents, and the right architecture (async pipelines, message queues, container-based compute) is what separates a working feature from a failing one. You will independently monitor production, triage issues quickly, and maintain a close read on how our platform is actually experienced by the clinicians, coordinators, and partners who depend on it. You’ll exercise pragmatic judgement, matching the technology to the business need. As we grow this will more and more include decisions around which AI models to use, and how to operationalize “token economics”.
Beyond technical ownership, you will shape how good engineering gets done at Trial Library. As AI coding tools and improved models change what's possible and how the whole team works, this role calls for someone who will continuously rethink what engineering excellence actually looks like. You are excited to transition the team into a world less bottlenecked by execution, and more by review and verification.
You keep a close pulse on how AI is changing software development, evaluate emerging practices critically, and champion the approaches that make sense for our organization .These best practices may include standards for how code reviews are done, how tests get written, what shared conventions need to exist, and where human judgment is most needed. You’ll mentor engineers and set development standards that stay relevant as the tools underneath all of us keep evolving.
We are looking for an engineer with significant backend depth, 8 or more years of experience, with at least a couple of years operating at staff scope or equivalent impact. Beyond tenure, what matters most is whether you have demonstrated the kind of high-impact ownership this role requires.
Deep AWS experience (Lambda, Fargate, SQS, RDS, and the surrounding ecosystem) and the ability to choose the right service for the right job
Proven track record of leveraging AI coding tools creatively and effectively to build production systems.
Demonstrated ability to take autonomous ownership, identifying and resolving systemic issues independently.
Startup experience, where you have built from scratch at an early-stage company and treated ambiguity as an opportunity rather than an obstacle
Strong systems thinking, encompassing backend architecture, APIs, databases, and scalability under real-world constraints.
Clear communication and influence, with the ability to explain tradeoffs to both engineers and non-engineers to earn trust through honesty
Healthcare alignment with a genuine interest in improving clinical trial access and health equity; HIPAA experience a strong plus
Our interview process typically includes four stages, followed by reference checks and an offer. Depending on the role, particularly for technical or senior positions, the process may vary slightly and can include a technical assessment, case study, presentation, practical exercise, additional interview conversations, or conversations with members of our leadership team and investors.
