Director, Engineering - Patient

Headway's mission is to build a new mental health care system that everyone can access. We've built technology that takes the hardest parts of mental healthcare — finding the right provider, navigating insurance, managing payments — and makes them simple. We're now one of the fastest-growing companies in healthcare, with more than 200,000 patients finding care through Headway and thousands of therapists using our platform to grow their practices.

The Patient Core Experience team owns how patients find the right therapist on Headway. From search through the first several sessions, our four engineering pods — Onboarding, Profiles + Checkout, Ranking + Relevance, and Activation — build the systems that make great therapeutic matches happen.

We operate a three-sided marketplace (patients, providers, payers) with real complexity and massive impact. We're evolving from a provider directory into an intelligent matching platform that uses communication style, data-backed expertise signals, and patient-reported outcomes to connect people with therapists who are genuinely right for them.

Principles that guide us:

• Matching quality is the foundation of therapeutic outcomes — we obsess over getting patients to the right provider
• We ship fast and learn fast, but we protect patient trust above all else
• We build for the whole marketplace: patients, providers, and payers
• ML powers our systems, but empathy drives our product

This role exists because we have hard, specific challenges that need senior engineering leadership to crack. Here's what your first year looks like:

Build an ML-powered matching system from the ground up. Our current matching is largely filter-based. We need to move to ML-powered ranking that incorporates provider communication style, expertise signals, and patient outcome data — without degrading the patient experience during the transition. You'll define the technical strategy for how we get there and own the execution across multiple pods.

Rearchitect team topology as the product evolves. We have four pods today, but the boundaries between ranking, activation, and onboarding will shift as we move toward intelligent matching. You'll need to evolve team structure, ownership boundaries, and technical interfaces as the product changes shape. This includes scaling the org from ~18 to ~25+ engineers while maintaining velocity and quality.

Close the gap between engineering output and patient outcomes. We have patient funnel metrics (intake-to-match, match-to-book, book-to-retained) but engineering doesn't yet co-own them tightly enough with Product and Data. You'll establish the operating model where engineering, product, and data science jointly own these metrics in a true triad — not one where engineering is an execution arm.

Define how your teams build software in the AI era. This isn't about adding a bullet point on AI tooling. You'll set concrete standards for how your ~30 engineers use AI-assisted development — how it changes code review, testing strategy, onboarding, and potentially team composition. You'll also own the AI/ML strategy for the product itself: where we use ML models vs. heuristics, how we handle explainability in a healthcare context, and how we build patient trust in AI-driven recommendations.

• →Set 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 ranking, reimagined patient onboarding, and patient activation systems
• →Build an engineering culture with clear standards for velocity, quality, and technical excellence — starting with honest assessment of where we fall short today

Required experience:

• 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
• Scaled an engineering org through a high-growth phase (25+ to 50+) while maintaining velocity and quality
• Technically strong enough to make sound architecture calls on ranking/ML systems, marketplace infrastructure, and consumer-facing surfaces — you don't write code daily, but you can review a system design and spot the problems
• Experience shipping ML-powered product features (search ranking, recommendations, personalization) at consumer scale
• Track record of moving business metrics (conversion, retention, engagement) through engineering-product partnership, not just delivering features on time
• Comfort working in a regulated industry where you can't just ship fast — you need to reason about bias, explainability, and patient safety in ML systems
• Practiced perspective on AI-augmented engineering workflows — you've led teams through adoption of AI development tools and have opinions on what changes and what doesn't

Nice-to-have:

• 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)
• Experience building LLM-based product features (conversational interfaces, intelligent triage, AI-assisted workflows)
• Experience rethinking team structure or hiring profiles in response to AI productivity gains

Our platform runs on Python (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.

You won't be writing code daily, but you'll need to engage deeply enough with these systems to make sound technical and organizational decisions.

• Solve a genuinely hard problem at the intersection of ML, marketplace dynamics, and healthcare — where getting matching right has real clinical consequences
• Shape an engineering org during a foundational transition, with high autonomy and strong executive support
• Co-own patient outcomes, not just engineering output
• Work at a company where the mission isn't marketing copy — patients are actually getting access to therapy they couldn't get before