Senior Engineering Manager (Ranking & Relevance)

Headway

7d • $265,200 - $331,500 • Remote

About The Position

Headway is building a new mental healthcare system that everyone can access by solving the biggest barrier to care: insurance. We have automated the administrative work like credentialing, claims, and payment reconciliation. Over 75,000 providers across all 50 states use our software, serving over 1 million patients. We are developing tools for therapists to manage their practices, improving the process of finding a therapist, and investing in platform foundations for scalability. We are a Series D company with over $325M in funding from investors like a16z, Accel, and Spark Capital, seeking exceptional individuals to help us achieve our mission and make this the most meaningful experience of their careers.

The Ranking & Relevance team at Headway is responsible for the retrieval and machine-learned ranking systems that determine which providers a patient sees, in what order, and why, across search, matching, and personalization. As a three-sided marketplace, the matchmaking system is crucial. A good match leads to patients who book and stay in care, providers who fill their caseloads with suitable clients, and payers whose members receive effective care. This team manages the trade-offs within this system. Currently, matching is largely filter-based, but we are rebuilding it into an intelligent system that leverages communication style, expertise signals, patient-reported outcomes, and behavioral data. Learning-to-rank has been implemented and has already positively impacted patient conversion, cancellations, provider activation, and payer utilization, marking the beginning of a significant evolution.

Requirements

• Led software and ML engineers together.
• Can identify if a model's problems stem from data issues.
• Owned a conversion or relevance metric end-to-end and can explain how a win was validated.
• Treats reliability and instrumentation as leadership work, not platform overhead.
• Wants to manage, viewing hiring, coaching, and holding a bar as core job functions.
• Can hold a hard trade-off with a peer organization without either capitulating or stonewalling.
• Has a point of view on how AI changes the way engineering teams work, not only what they ship.
• 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.

Responsibilities

• Lead eight engineers, a mix of senior software and machine-learning engineers.
• Partner daily with a staff product manager, two data scientists, and the payer and provider engineering organizations.
• Shape the Ranking & Relevance systems with technical judgment.
• Make matching intelligent rather than filter-based by leading the shift to a system that learns from communication style, expertise signals, outcomes data, and real behavior.
• Decide where a model is appropriate and where a simpler rule is better.
• Resolve the three-sided objective where patient conversion, provider activation, and payer efficiency currently compete within the ranker.
• Land a joint objective that the whole marketplace can agree to, managing both the modeling and the negotiation.
• Rank for outcomes, not just bookings, by taking the team into outcome-aware ranking using patient-reported outcomes and measured expertise.
• Define quality carefully, explain it plainly, and defend it.
• Make ranking quality provable and fast to iterate by building the evaluation, monitoring, and drift detection systems.
• Build the team and set the bar for applied ML work, including model review, experiment decision-making, and the role of AI in engineering teams.
• Hire into a strong team and develop engineers.
• Hold a hard trade-off with a peer organization without capitulating or stonewalling.
• Contribute to the point of view on how AI changes engineering teams and their work.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed