Original listing text, shown exactly as published by the company.
Key Responsibilities
- Set AI and engineering strategy for the core patient experience in partnership with Product and Data Science leadership
- Lead 3 Engineering Managers; scale the org from ~18 to ~30+ engineers over the next 18 months
- Co-own patient funnel metrics with your Product and Data counterparts — not just deliver against them
- Drive delivery of ML-powered matching, reimagined patient onboarding, and patient activation systems
- Own the AI product roadmap for your pods: where we use ML, where we use LLMs, how we reason about explainability and patient safety
- Build an engineering culture that uses AI in the workflow and builds AI into the product — as two distinct, equally important practices
What You Bring
Required
- 10+ years of software engineering experience, 5+ years managing engineering managers
- Led engineering for a consumer or marketplace product where search, matching, ranking, or personalization was core to the business
- Shipped ML-powered product features at consumer scale and can make sound architecture calls on how they get built
- A practiced, opinionated perspective on AI-augmented engineering workflows — you've led teams through adoption and have concrete views on what changes in code review, testing, hiring, and engineering culture
- Track record of moving business metrics (conversion, retention, engagement) through engineering-product partnership, not just delivering features on time
- Technically credible enough to engage deeply on ML systems, marketplace infrastructure, and consumer-facing architecture — you don't write code daily, but you can spot the problems in a design review
- Comfort working in a regulated environment where you must reason about bias, explainability, and patient safety in ML systems
Strongly preferred
- Experience building LLM-based product features (conversational interfaces, intelligent triage, AI-assisted workflows) — this is where patient-facing AI is heading and we want someone who has been there
- Experience rethinking team structure or hiring profiles in response to AI productivity gains — you've thought through what a high-performing team looks like when AI is a meaningful part of how code gets written
- Healthcare experience or other regulated industries where data sensitivity and clinical consequences raise the stakes
- Experience with marketplace dynamics (supply/demand balancing, multi-sided incentive design)
Our StackPython (Django/FastAPI) and TypeScript/React on the frontend. Elasticsearch powers search and ranking. PostgreSQL and Redis handle data storage and caching. We use dbt and Snowflake for data pipelines, Temporal for workflow orchestration, and custom ML models for matching. Everything runs on AWS.
For AI development, we use Claude Code and Cursor across the engineering org and are actively evolving our standards for AI-assisted workflows. You'll be setting the direction here, not inheriting a finished playbook.
You won't be writing code daily, but you'll engage deeply enough with these systems to make sound technical and organizational decisions.
You'll Love This Role If You Want To
- Build AI products with genuine clinical impact — where better matching means better mental healthcare for real people
- Lead the transition to AI-era engineering in practice, not just in principle — with the autonomy to define what that means for your teams
- Co-own patient outcomes, not just engineering output
- Shape an engineering org during a foundational transition, with strong executive support and a clear mission
- Work at a company where the mission isn't marketing copy — patients are actually getting access to therapy they couldn't get before
