Director, Engineering

The Problems You'll Solve

• Build the AI-powered matching engine that defines Headway's next chapter. Own the technical strategy and execution for moving to ML-powered ranking — incorporating provider communication style, clinical expertise signals, and patient outcome data. Decide 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.
• Ship AI product features that make the patient journey feel intelligent. 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. 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. Set concrete standards for how your ~30 engineers use AI in their workflow. Develop a real POV on how AI changes code review, testing strategy, PR standards, onboarding, and what skills you hire for.
• Close the gap between engineering output and patient outcomes. Establish the operating model where engineering, product, and data science jointly own patient funnel metrics (intake-to-match, match-to-book, book-to-retained).
• Evolve team structure as the product evolves. 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.

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
• 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

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
• Technically credible enough to engage deeply on ML systems, marketplace infrastructure, and consumer-facing architecture
• 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)
• Experience rethinking team structure or hiring profiles in response to AI