Staff / Principal Product Manager, Ranking

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 This Role

One million patients a month ask for a referral to a mental health provider. When they search on Headway, the order of results decides who books and who gives up. You own that order.

The scope is patient–provider matching: the retrieval and ranking systems, the strategy for where they go next, how we think about our data strategy and platform to . Your mission: maximize successful matches. Not clicks. Not bookings. Patients who connect with a provider, stay in care, and get better.

It's the most technical PM seat in the Patient org. The ranking stack is ML-powered, and the roadmap runs deep: learned relevance models, semantic and free-text search, matching built on better evidence of each provider's expertise. You'll set that roadmap and get the executive team behind it, working with a dedicated pod of search and ML engineers and embedded data scientists. This is not a role for someone who needs a well-defined problem. The best candidates have a track record of finding the right problem and convincing the organization to solve it.

Problems You'll Solve:

Understanding what patients actually need. Patients rarely arrive knowing the right filters. We're building search that lets someone describe what they need in their own words and still surface the right providers. Semantic search, LLMs, and embeddings are all in play.

Ranking for the relationship, not the click. A ranker that optimizes for conversion alone can win the booking and lose the patient. Rebuild it to optimize for care relationships that last.

Expertise. Every patient should land with a provider who has real experience helping people like them. Getting trustworthy expertise signals into matching, fairly to providers, is an unsolved problem. It's yours.

Marketplace health. Patients need care fast. Providers across the network need to build caseloads. When those two pull in opposite directions, you make the call.

You Will:

Set the strategy for search, retrieval, and ranking, and get engineering, data science, design, clinical, and the executive team behind it.

Run the experimentation program end to end: hypotheses, objective functions, guardrail metrics, honest readouts. Report the losses too.

Build the data, evaluation, and ML platform foundations that unlock the next two to three years of matching work.

Work daily with ML engineers and data scientists on model objectives, features, and evaluation. You won't write the models. You will be a real thought partner on what they optimize.

Carry ranking's priorities across the company. Ranking touches patient experience, provider growth, clinical quality, and payer partnerships; resolving those competing priorities is the job, not a distraction from it.

Set the bar for technically grounded product work across the PM team.

Who You Are:

You have 10+ years of product management experience , including hands-on ownership of search, ranking, recommendations, or marketplace matching at scale.

You've shipped ML ranking or relevance systems to real users. You can discuss objective function design, feature development, and offline vs. online evaluation from experience.

You can hold an architecture conversation with ML engineers, challenge modeling assumptions, and make informed build-vs-buy calls.

You've lived marketplace trade-offs and understand that ranking allocates opportunity across one.