Staff Software Engineer - Experimentation
Headway.co · USA
About this role
## Staff Software Engineer — Experimentation ### About Headway Headway’s mission is to make mental healthcare accessible for everyone. In the US, many people who need therapy can’t afford it because most providers don’t accept insurance. Headway is building a new mental healthcare system by solving the insurance barrier and creating software that helps therapists run their practices end-to-end. Headway is a Series D company ($325M+ in funding) and supports **90,000+ providers** across **all 50 states**, serving **2.6M+ patients**. ### Engineering Principles - **Focus on the mission**: software as a means of effecting change. - **Ship small, learn fast**: iterate quickly to learn what users need. - **Everything is a product**: build with end-users in mind. - **Optimize for trust**: create an environment where engineers can ask questions and learn from mistakes. ### Tech Stack (examples) - **Languages**: Python 3, TypeScript - **Frameworks/Libraries**: FastAPI, SQLAlchemy, React, Remix - **Experimentation & Analytics**: LaunchDarkly, Statsig, dbt, Snowflake - **Datastores**: Postgres, Redis - **Infrastructure**: AWS (Fargate, ECS, S3, etc.), Cloudflare, Spark, Kafka - **Monitoring**: Datadog, PagerDuty, Sentry - **Version Control**: GitHub --- ## About the Role Headway is looking for a **Staff Software Engineer** to **own how Headway runs experiments**. Experimentation is central to how teams improve patient and provider experiences—covering topics like: - how patients find a therapist - how quickly new providers get up and running - whether AI products are working - how clinical outcomes data proves care effectiveness This is a **hands-on technical leadership** role with unusually wide reach. You’ll design and build core experimentation primitives, including: - how users are **assigned** to experiments - how we **record what someone actually saw** - how we **recognize the same person across devices** and between anonymous browsing and signed-in sessions You’ll also: - decide the **long-term experimentation + feature flagging stack** (evaluating LaunchDarkly/Statsig today) - make **AI a real part of the workflow**, including validating experiment setup before launch and catching broken experiments automatically - evolve experimentation to support **market-based testing** (multi-sided marketplace needs, geographic markets, long holdouts, and robust analysis) The job is both a **platform** and a **program**: - **Platform**: technical foundations (assignment, exposure, data pipelines) - **Program**: improve how the whole company experiments via defaults, review, documentation, and education You’ll work closely with **Data Science** (statistical methodology) and teams responsible for **event tracking and metric definitions**—because trustworthy results depend on every link in the chain. --- ## Who You Are You’re a Staff-level engineer who measures impact by what other teams can now do. You: - think in **systems** and raise the bar for measurement quality - have owned an **experimentation or feature flagging stack end-to-end** (not just used one) - built non-trivial components like: - persistent assignment - exposure that reflects what users actually saw - identity resolution across sessions/devices - diagnostics that catch broken experiments early - are fluent enough in statistics to partner with data scientists and distinguish **weak vs. invalid** results - have debugged hard measurement issues (uneven traffic splits, us
Listing freshness
CronJobs last confirmed this listing 1h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.