Job Description
Who you are 7+ years of backend engineering experience in production environments Strong experience building and operating backend systems at scale Deep expertise in backend architecture, distributed systems, and microservices Excellent written and verbal communication Strong ownership mentality and the ability to proactively identify and solve problems Comfort operating as a lead and wearing multiple hats across software, infrastructure, reliability, and cross-functional execution Strong debugging and root-cause analysis skills A track record of improving engineering quality, not just delivering features A high level of care, accountability, and pride in your work Strong backend development experience in languages relevant to our stack Experience designing and operating microservices, REST APIs, and event-driven systems. Strong database skills, ideally with PostgreSQL Experience with cloud infrastructure and infrastructure-as-code, especially Terraform Experience with CI/CD, deployment pipelines, and production operations Strong experience with monitoring, observability, tracing, logging, and alerting Experience handling incidents, production debugging, and postmortem-driven improvement Hands-on experience iterating prompts, retrieval, and evaluation in production — not just calling a model API Day-to-day fluency with AI coding tools (Claude Code, Cursor, or equivalent) as part of your normal development loop Experience in healthcare or health-tech. Familiarity with standards and healthcare data models such as FHIR and LOINC Experience supporting systems used by both internal product teams and external customers or partners What the job involves We are hiring a Lead Backend Platform Engineer to own and strengthen a broad backend surface area spanning app APIs, partner APIs, EHR integrations, AI services, cloud infrastructure, and observability Own backend services that power our app and partner-facing products Lead design, implementation, and maintenance of APIs, microservices, EHR integration layer and service-to-service workflows Manage the AI infrastructure that powers our health intelligence and evaluation harness Improve production reliability, observability, alerting, incident response, and debugging workflows Drive architectural improvements that simplify service boundaries and reduce operational fragility Improve the quality, consistency, and maintainability of backend systems across the stack Partner closely with product, frontend, and AI to deliver robust systems Help define and enforce strong engineering standards around service design, testing, deployment, monitoring, and operational ownership Step into ambiguous, cross-functional problems and drive them to resolution with minimal oversight What success looks like: Within 90 days, a new partner can integrate against our API in roughly a week with you as their primary technical contact When a user-facing failure happens, you can localize the cause across ingestion, AI generation, and infrastructure and fix it without escalating Prompt and pipeline changes ship behind a regression evaluation that catches quality drops before production Auth, ingestion, and report incidents trend down because you've added the missing tracing, limits, and tests The team has a stronger technical leader who communicates clearly, takes ownership, and raises the bar This person will report directly to the CTO and work very closely with our AI Lead. For the right person, this is an opportunity to have outsized impact: solving hard systems problems, improving the technical foundation of the company, and helping shape how January builds production-grade AI products Real ownership across the stack: You'll own the systems that connect a person's clinical and lifestyle data to AI-generated insights — used by both our consumer app and the enterprise partners we serve A team that can back the work: You'll work alongside Stanford-trained scientists, AI researchers, and operators who've shipped at scale, reporting to the CTO with the AI Lead as a close partner Research-credentialed product: Our clinical reasoning model has published benchmarks that hold up against the largest general-purpose LLMs. We treat scientific credibility as a product requirement, not marketing Our team collaborates in real-time during standard working hours Pacific Time