Job Description
We are building an early-stage healthcare software product focused on structured clinical reasoning, not chatbots. The goal is a small, reliable backend service that accepts standardized inputs (PROMIS scores + minimal clinical context) and returns a strictly validated JSON output representing clinical reasoning for non-specific low back pain in a Workers’
Compensation context. This is not a full app. It is not UI-heavy. It is a precision backend problem. If you enjoy clean APIs, schemas, validation, and test-driven development, this will be a good fit.
See the following links to read more about PROMIS: What you will build (V0) A single POST endpoint: /generate-plan Input: JSON (PROMIS Physical Function / Pain Interference + clinical + WC context) Output: JSON that must validate against a provided JSON Schema LLM call with: strict structured output retry-on-failure logic low temperature Local evidence card store (simple JSON or DB) Automated tests (10 scenarios provided) Dockerfile + README No EMR integrations. No frontend required (unless you strongly prefer a minimal one).
Tech Stack (flexible) Backend: Node.js (Express) or Python (FastAPI) Schema validation: AJV / pydantic LLM: OpenAI or compatible API Tests: Jest / Pytest Deployment: Render, Railway, Fly.io, or Docker-local Non-negotiables Strict JSON schema validation (no free-text output) Clean code + repo access from day 1 Automated tests Clear README No PHI storage or logging Willingness to work from a detailed spec Nice to have Experience with LLM structured outputs Healthcare or regulated-domain experience Opinionated about validation and failure handling (important) In your first message, please include: A short description of a backend API you’ve built with strict validation Which stack you’d use here and why How you would handle LLM output that fails schema validation
Your availability over the next 2–3 weeks Applications that don’t address these points will not be considered.
Budget Fixed-scope, milestone-based. Looking for someone efficient and pragmatic rather than flashy. Apply tot his job