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.

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 Staff Machine Learning Engineer to lead the development of Headway’s ranking and relevance models. You will design and build the systems that decide how patients and providers are matched, using search, recommendation, and personalization to help patients through their care journey. You will set technical direction, build the initial stack, and mentor future ML teammates.

What you’ll do

At Headway, we have built the largest network of therapists who accept insurance in the country. One of our most important challenges is ensuring that patients find the right therapist for them quickly and effectively. To do this, we are investing deeply in search, ranking, and relevance systems powered by machine learning. As a Staff Machine Learning Engineer on the Patient team, you will focus on:

• Matching and ranking: Design and scale ML models that power how patients discover and connect with providers.
• Personalization: Use patient signals, provider attributes, and outcomes data to improve matching accuracy.
• ML infrastructure: Establish the foundation for experimentation, model training, deployment, and monitoring across Headway.
• Mentorship and technical leadership: Guide junior ML engineers and data scientists as the team grows, setting the bar for ML practices at Headway.
• Cross-functional impact: Partner with product, engineering, and data science to define success metrics, design A/B experiments, and influence product strategy.

Who you are

• You have 6+ years of experience in applied ML roles, with at least 1 year working on ranking, relevance, search, or recommender systems.
• You are fluent in Python and have experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, CatBoost, etc.).
• You have taken ML models from research/prototype to production at scale.
• You are comfortable designing and running A/B experiments, and you know how to choose the right offline and online metrics.
• You combine product intuition with technical depth. You care about making the patient experience meaningfully better, not just optimizing a model score.

Nice to have

• Experience with matching systems or 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.

Interview process

• Initial screen: You’ll connect with someone in recruiting to learn more about the team, Headway’s mission and growth, and to get a better idea of your background.
• First round: You’ll meet with an engineer on the team to do live coding and learn more about the engineering team.
• Final rounds: You’ll meet several more team members for technical and non-technical interviews and leave with a fuller picture of what it’s like to work at Headway.
• References and the offer: We’ll share details including employee equity and messages from excited future team members.

Compensation and benefits

The expected base pay range for this position is $269,790 – $317,400, based on qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for performance-based variable compensation and an equity grant, depending on the position and level.

Note: We are committed to offering a comprehensive and competitive total rewards package, including health and wellness benefits, retirement savings, and meaningful ownership opportunities through equity.

• 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 plus a Holiday Break during the week between December 25 and 31
• Flexible PTO
• Employee Assistance Program (EAP)
• Training and professional development

We believe a team should reflect the diversity of the problem we’re solving. Headway is committed to inclusion and provides reasonable accommodations for applicants with disabilities. If accommodations are needed to participate in the interview process or employment, please inform the recruiter.

Headway employees work remotely across the US, with the option to work from offices in New York City, San Francisco and Seattle. Headway participates in E-Verify. To learn more, click here.

Headway Custom Demographic Questions

We believe in building a diverse, equitable and inclusive team. Please consider answering the following demographic questions. You may choose “I don’t wish to answer” for any question. Your responses are used in aggregate to improve our process and will not be linked to your application or hiring decision.

Your responses will not be linked to your specific application and will not be used in the hiring decision.