Machine Learning Engineer

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.

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.

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.

• 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.

• 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 (Tensor Flow, PyTorch, Scikit‑learn, Cat Boost, 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.

• 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, Sage Maker, and Outerbounds.

• 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 round:
  You’ll meet with an engineer on the team to do some 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:
  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.