Software Engineer

The product\n * Internal practice-management and clinical-operations platform for a telehealth provider.

It sits as a middleware layer between our staff and the Healthie EHR\n * Python/FastAPI backend + Next.js/React frontend in a monorepo, handling live PHI for real patients - not a greenfield project and not a prototype.\n * Small team, high ownership.

You will take features end-to-end: data model API UI tests deploy.

You will not be handed pre-decomposed tickets.\n \n What you'll work on\n * Patient intake, chart notes, scheduling, and billing workflows used daily by clinical staff.\n * Third-party integrations: EHR, clearinghouse/eligibility vendors, CRM, and form webhooks.\n * An in-app clinical AI Copilot built on streaming LLM APIs.\n \n Required experience\n * Python 3.12 + FastAPI\n * TypeScript + Next.js 15 (App Router) + React 19\n * Zustand for client state, or other strong global state management experience\n * GraphQL as a consumer - writing and debugging queries and mutations against a third-party API you do not control\n * REST/HTTP integration\n * Unit testing with Vitest and Pytest\n * Redis caching\n * PostgreSQL / Supabase\n * Git fluency\n \n Strong plus\n * US healthcare domain knowledge: insurance eligibility (EDI 270/271), payer and plan-type classification, Medicare vs.

Medicare Advantage vs.

Medicaid, MBIs, CPT/ICD-10, CMS-1500 claims.\n LLM application engineering: OpenAI/Anthropic APIs, streaming over SSE,tool/function calling, prompt design, token and cost control, multi-turn session persistence.\n * CRM and webhook integration work: Zoho CRM REST v8, OAuth 2.0 client credentials, form webhooks.\n Observability: Sentry, PostHog, and log-driven debugging in a hosted environment.\n \n How we work\n * Strong written English.

Most communication is asynchronous: PR descriptions, design proposals, written status.

You need to explain a trade-off in writing without a call.\n * Escalates early.

When a spec is ambiguous or an approach is blocked, you raise it within hours instead of shipping a guess three days later.\n * Reads before writing.

The repo carries extensive internal documentation —architecture notes, per-domain summaries, prior implementation plans.

Search that context before proposing a design.\n * Comfortable using AI coding agents (Claude Code)\n as part of the normal workflow — planning, implementing, and verifying with agent assistance, while staying fully accountable for the code you submit.\n * Updates internal documentation as part of finishing a task, not as an afterthought.\n \n Not required\n * Mobile, Kubernetes/DevOps, ML training, or data-engineering experience.\n * Prior US healthcare experience is a strong plus, but not a hard gate for an otherwise excellent Python/TypeScript integration engineer.