Director, Engineering; Patient

Headway's Mission

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.

We believe AI is the unlock for our next phase — more intelligent matching, more personalized patient experiences, and a fundamentally different way of building software. This role sits at the center of that transition.

This role exists because we have hard, specific challenges that need senior engineering leadership to crack.

Build the AI-powered matching engine that defines Headway's next chapter. Our current matching is filter-based. You'll own the technical strategy and execution for moving to ML-powered ranking — incorporating provider communication style, clinical expertise signals, and patient outcome data. This means deciding when to use ML models vs. heuristics, how to reason about explainability and bias in a healthcare context, how to A/B test matching quality without degrading patient experience, and how to build patient trust in AI-driven recommendations.

Getting this right will directly improve outcomes for every patient on Headway.

Ship AI product features that make the patient journey feel intelligent. Matching is just the start. There's significant opportunity to use LLMs and generative AI to improve how patients understand their options, how we guide them through onboarding and intake, and how we keep them engaged through the early sessions where drop-off risk is highest. You'll own the AI product strategy for your pods — where we go beyond ML ranking into generative and agentic approaches, and how we do it responsibly in a regulated healthcare environment.

Define what AI-era engineering looks like for your teams. You'll set concrete standards for how your ~30 engineers use AI in their workflow — and this isn't a generic "adopt Cursor" mandate. You'll develop a real POV on how AI changes code review, testing strategy, PR standards, onboarding, and what skills you hire for. You've led teams through this transition before and have specific, battle-tested opinions on what changes and what doesn't.

You'll be setting the standard, not delegating it.

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 — a true triad, not one where engineering is an execution arm.

Evolve team structure as the product evolves. The boundaries between ranking, activation, and onboarding will shift as we move toward intelligent matching. You'll evolve team topology, ownership boundaries, and technical interfaces as the product changes shape — while scaling from ~18 to ~25+ engineers without losing velocity or quality.

• 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

• 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…