Staff Software Engineer
Staff-level engineer building LLM/ML systems for clinical documentation review, risk detection in healthcare claims, and provider-patient matching at a mental healthcare platform.
About the job
What you could work on
- Clinical Feedback: Architect the LLM-powered systems that review clinical documentation at scale and deliver helpful, real-time feedback to providers.
- Detecting Risk in Healthcare Claims: Design the ML/LLM systems that spot patterns of inappropriate billing, over-utilization, and low-quality care across millions of appointments and claims.
- Measuring & Elevating Clinical Quality: Build the systems that systematically review every prescriber in our network against clinical quality standards.
- Patient Needs & Provider Fit: Build ML systems that understand how complex a patient’s needs are and match them to providers best suited to help.
- Trustworthy AI in Healthcare: Build the evaluation frameworks, optimization loops, and observability that keep LLM-powered features reliable and safe in a regulated environment.
- Technical Foundations & Mentorship: Set the technical bar for the Payers & Outcomes group (~12 engineers today, growing to 30–50). Lay the architectural foundations other teams will build on.
Our Stack
- Languages: TypeScript, Python 3
- Frontend: React, Remix, Next.js
- Backend & Storage: FastAPI, SQLAlchemy, Postgres, Redis, Snowflake
- Infrastructure: AWS, Temporal, Kafka
- Ops: Datadog, Sentry, PagerDuty
- AI Tools: Cursor, Claude, Gemini, Eddy (in-house cloud agent harness)
Who You Are
- End-to-End Product Builder: Care about API architecture and user experience. Seamlessly context-switch between backend logic and frontend state management.
- Owns Outcomes, Not Tasks: Turn ambiguous problems into shipped software. Define the solution, pull in the right people across teams, and keep things moving when priorities shift.
- Sets the Technical Standard: Make technical decisions that optimize for maintainability and scale. Proactively improve team's engineering velocity through tooling, automation, or patterns.
- Mentor by Default: Pull engineers into design discussions. Code reviews teach, not just gatekeep. Ensure knowledge gets shared across the team.
- Ships Fast, Iterates Faster: Move from problem statement to working solution quickly. Ship a good v1 this week rather than a perfect v1 next month.
- Quick Study in Complex Domains: Dive into unfamiliar domains and build a working mental model fast.
- Motivated by Impact: Apply engineering skills to problems that matter.
- Communicates with Precision: Communicate clearly and concisely in Slack threads, design docs, or cross-team meetings.
- AI Frontier Tinkerer: Experiment with applying LLMs to real workflows. Bring a builder's mindset around automation and responsible shipping in healthcare.
Skills
TypeScript, Python, React, Remix, Next.js, FastAPI, Sqlalchemy, Postgres, Redis, Snowflake, AWS, Kafka, LLMs, Machine Learning
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