Senior Machine Learning Engineer

Headway•San Francisco, NY

3d•$218,500 - $273,125

About The Position

Headway’s mission is a big one – to build a new mental health care system everyone can access. We’ve built technology that helps people find great therapists with the first software-enabled national network of providers accepting insurance. 1 in 4 people in the US have a treatable mental health condition, but the majority of providers don’t accept insurance, making therapy too expensive for most people. Headway is building a new mental healthcare system that everyone can access by making it easy for therapists to accept insurance and scale their practice. Headway was founded in 2019. Since then, we’ve grown into a diverse, national network of over 60,000 mental healthcare providers across all 50 states who run their practice on our software and have served over 1 million patients. We’re a Series D company with over $325m in funding from a16z (Andreessen Horowitz), Accel, GV (formerly Google Ventures), Spark Capital, Thrive Capital, Forerunner Ventures and Health Care Service Corporation. 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 the team The Ranking & Relevance team’s mission is to help every patient find the right provider for their needs. We are building the matching system that powers this connection, from search and discovery to ranking and personalization. Our goal is to combine cutting-edge machine learning with a deep understanding of patient and provider experience. About the role We are looking for a Senior Machine Learning Engineer to be a core contributor to Headway's ranking and relevance systems. You will design, build, and ship ML models that determine how patients discover and connect with therapists — using search, recommendation, and personalization techniques to improve matching quality across the care journey. In this role, you will work with significant autonomy on scoped problem areas, contribute to technical direction, and collaborate closely with product, engineering, and data science to drive measurable outcomes for patients and providers.

Requirements

You have 5+ years of experience in applied ML roles, with at least 3 years of meaningful hands-on modeling work in ranking, relevance, recommendations, search, or personalization.
You are fluent in Python and experienced with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or CatBoost.
You have taken ML models from prototype to production and understand what it takes to make them reliable, maintainable, and performant at scale.
You are comfortable designing and running A/B experiments, selecting appropriate metrics, and interpreting results with rigor.
You bring product intuition alongside technical depth — you care about making the patient experience meaningfully better, not just improving an offline metric.
You work well in collaborative, cross-functional environments and can communicate technical tradeoffs clearly to non-ML stakeholders.

Nice To Haves

Experience with search, discovery, matching systems or consumer facing personalization.
Familiarity with modern retrieval techniques (vector search, embeddings, semantic search).
Exposure to ML infrastructure such as feature stores, model monitoring, and retraining pipelines.
Experience with Metaflow, SageMaker, and Outerbounds.
Background in healthcare, marketplace, or other B2C domains where user-provider or user-product fit is the core product problem.

Responsibilities

Matching and ranking: Build and iterate on ML models that power how patients discover and connect with providers — from candidate generation to final ranking.
Personalization: Leverage patient signals, provider attributes, and outcomes data to improve matching accuracy and relevance over time.
Model development end-to-end: Own the full lifecycle of models you build — from offline evaluation and experimentation to production deployment and monitoring.
Experimentation: Design and analyze A/B tests, define the right offline proxies and online metrics, and translate results into product decisions.
Cross-functional collaboration: Partner with product managers, software engineers, and data scientists to scope problems, align on success metrics, and ship improvements that move the needle for patients.
Raising the bar: Contribute to ML best practices on the team — through code reviews, documentation, and sharing what you learn from the systems you build.

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