Senior Engineering Manager (Ranking & Relevance)

1 in 4 people in the US have a treatable mental health condition, but most providers don't accept insurance, making therapy too expensive for most people. Headway’s mission is to fix this by building a new mental healthcare system everyone can access. We started by solving the biggest barrier to care: insurance. The admin work - credentialing, claims, payment reconciliation - is a nightmare. We've automated that. But we're going further. Over 75,000 providers across all 50 states run their practice on our software, serving over 1 million patients. We are building the best tools for therapists to run their entire practice, reimagining the experience of finding a therapist, and investing in the platform foundations to enable this at scale. We aren't just a billing layer; we are becoming the platform where care actually happens. We're a Series D company with $325M+ in funding (a16z, Accel, Spark Capital, etc.), looking for exceptional people to help us achieve this mission. We want your time here to be the most meaningful experience of your career. Join us, and help change mental healthcare for the better. About Ranking & Relevance at Headway Every patient who comes to Headway is asking one question: which of these therapists is right for me? Ranking & Relevance owns the answer. We build the retrieval and machine-learned ranking systems that decide which providers a patient sees, in what order, and why - across search, matching and personalization. Headway is a three-sided marketplace, and the engine that powers that marketplace is the match-making system. A good match means a patient who books and stays in care, a provider whose caseload fills with the clients they are genuinely good at, and a payer whose members get effective care. Those goals overlap most of the time and compete some of the time, and this team owns that tradeoff in code. It is the hardest product problem at Headway, and match quality is not a vanity metric: the gap between a good match and a poor one is the difference between a patient who stays in care and one who gives up on it. Today our matching is still largely filter-based. We are rebuilding it as an intelligent system that uses communication style, data-backed expertise signals, patient-reported outcomes and real behavioral signals to surface the right provider for each patient at the moment they are ready to book. Learning-to-rank went live this year and has already moved patient conversion, cancellations, provider activation and payer utilization. That is the first mile of a much longer road. Principles that guide us Mutual matches, not clicks. We optimize for matches that hold up months later, not impressions that convert today. Measured or it didn't happen. Every change ships behind an experiment with a decision rule agreed in advance. Own the outcome, not the model. The team that trains a model ships it, runs it and improves it, pager included. Clinical stakes are engineering stakes. A ranking regression is not a dashboard problem. It changes who gets care, and how soon. About this role We are hiring a Senior Engineering Manager to lead Ranking & Relevance. You will lead eight engineers today - a mix of senior software and machine-learning engineers. You will partner daily with a staff product manager, two data scientists, and the payer and provider engineering organizations whose outcomes depend on your ranker. This is a domain-depth role, not a span-of-control role. The systems are young enough that your technical judgment will shape them, and consequential enough that a ranking regression is visible to the whole company within days. You will report to the Director of Engineering for Core Patient Experience and work alongside the managers for onboarding, profiles and checkout, and activation, so what your team builds lands in the same patient journey theirs does. The problems you'll solve in your first year Make matching intelligent rather than filter-based. Ranking today leans on filters and hand-tuned boosts. You will lead the shift to a system that learns from communication style, expertise signals, outcomes data and real behavior, and you will decide where a model belongs and where a simpler rule is honestly better. Resolve the three-sided objective. Patient conversion, provider activation and payer efficiency currently compete inside the ranker, and each improvement partially cancels another. You will land a joint objective the whole marketplace can agree to, with the modelling and the negotiation both on you. Rank for outcomes, not just bookings. Today's models predict who books. What matters is who stays in care and gets better. You will take the team into outcome-aware ranking using patient-reported outcomes and measured expertise, in a domain where quality has to be defined carefully, explained plainly and defended. Make ranking quality provable and fast to iterate. Offline evaluation and online results do not yet agree closely enough to make quick decisions. You will build the evaluation, monitoring and drift detection that let the team ship weekly and trust the read. Build the team and the bar. You will hire into a team that is already strong and set what applied ML work looks like here: how models get reviewed, how experiments get decided, and how much of the work AI should be doing for us rather than to us.