Sprinter Health is looking to solve the problem of patients skipping preventive or chronic care due to accessibility issues, which leads to significant avoidable costs in the healthcare system. The company aims to bring healthcare directly to patients' homes and is seeking to evolve its patient booking process from primarily outbound phone outreach to a more product-led motion.
Requirements
- Spent 5+ years as a software engineer, including 2-3 years on a growth or experimentation-focused team
- Built and iterated on backend systems that power experimentation, messaging, funnels, or activation flows
- Shipped and measured experiments that moved core metrics like activation, conversion, retention, or booking
- Used data to identify drop-offs, size opportunities, and prioritize what to build next
- Youve shipped experiments that directly moved activation, conversion, or booking metrics
- Youre comfortable owning the full loop sizing, building, launching, and measuring
- Youve worked on products where growth is driven through the product, not just outbound channels
Responsibilities
- Design and ship experiments that drive patient bookings from message copy tests and outreach timing to new in-product booking flows
- Optimize multi-channel engagement across SMS, phone, email, and mail to reach patients who typically dont schedule care
- Run rapid brainstorms and sizing exercises to prioritize the next set of growth bets
- Define and monitor funnel metrics to understand drop-off, activation, and conversion
- Share insights through clear reporting and experiment reviews with cross-functional partners
- Test unconventional tactics (like door-knocking pilots) and simulate outreach volume to match clinical supply and demand
- Built and iterated on backend systems that power experimentation, messaging, funnels, or activation flows
Other
- Mentored earlier career engineers and have held pod-level ownership before on projects
- Collaborated with product, design, data, or ops to turn insights into shipped features
- Balanced speed and impact in a startup or high-growth environment
- You move fast with data, scrappy tooling, and lightweight experiments
- Youve operated in high-ambiguity, zero-to-one, or PLG environments where playbooks didnt exist
