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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

Who you are

• You have led software and ML engineers together, and you can tell which of a model's problems is a data problem.
• You have owned a conversion or relevance metric end to end and can explain how you knew a win was real.
• You treat reliability and instrumentation as leadership work, not platform overhead.
• You want to manage. Hiring well, coaching engineers and holding a bar are the job, not the tax on the job.
• You can hold a hard tradeoff with a peer organization without either capitulating or stonewalling.
• You have a point of view on how AI changes the way engineering teams work, not only what they ship.

Experience we're seeking

• 4+ years managing engineers, including senior ICs and machine-learning engineers, with a track record of hiring and developing them.
• 5+ years as a software or ML engineer building production systems at scale.
• Hands-on ownership of search, ranking, recommendation or personalization systems - retrieval, learning-to-rank, feature pipelines, online experimentation.
• Experience in a marketplace or multi-stakeholder product where competing incentives had to be reconciled in the product itself.
• Comfort in a domain where the outcome is clinical, the data is sensitive, and being roughly right is not good enough.

You'll love this role if you want to

• Own the highest-leverage machine-learned system in a healthcare marketplace, with a direct line from your team's work to whether a patient finds the right therapist.
• Build the ranking discipline at a company early enough for your standards to become the standards.
• Work on a problem where the business metric and the human outcome point the same direction.
• Define what AI-augmented engineering looks like on a team where the product itself is machine learning.

Our interview process

After you apply to Headway, here are some details of what to expect during the interview process.

• Initial screen: You'll connect with someone in recruiting so you can learn more about the team, Headway's mission and exciting growth, and we can get a better idea of your background.
• First rounds: You'll meet with an engineering leader on the team to go deeper into your team and people leadership experience. You'll also complete an AI Coding round - a collaborative session where we're less focused on syntax and more on how you think through problems and work with AI tools in a technical context.
• Final rounds: You'll meet team members across product and engineering leadership for multiple behavioral interviews and one more technical interview (System Design), leaving you with a fuller picture of what it's like to work at Headway.
• References and the Offer: Our favorite part of the process! We'll send over all of the details, including specifics on employee equity, and congratulatory messages from excited future team members!

Compensation and Benefits:

The expected base pay range for this position is $265,200 - $331,500, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level.

We are committed to offering a comprehensive and competitive total rewards package, including robust health and wellness benefits, retirement savings, and meaningful ownership opportunities through equity. Compensation decisions are made holistically, ensuring fairness and alignment with market benchmarks while recognizing individual contributions and potential.

• Benefits offered include:
  
  • Equity compensation
  • Medical, Dental, and Vision coverage
  • HSA / FSA
  • 401K
  • Work-from-Home Stipend
  • Therapy Reimbursement
  • 16-week parental leave for eligible employees
  • Carrot Fertility annual reimbursement and membership
  • 13 paid holidays each year as well as a Holiday Break during the week between December 25th and December 31st
  • Flexible PTO
  • Employee Assistance Program (EAP)
  • Training and professional development