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.
264k – 330k
Remote7+ YOEML Engineering
About the role
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.
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.
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