Staff Software Engineer, Growth
Hinge Health
| Company | Hinge Health |
| Category | Engineering |
| Location | San Francisco |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Not stated |
| Salary | USD 197k–275k |
| Posted | 16 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
THE ROLE
We’re looking for a Staff Engineer to join our Growth team and scale the programs that bring new members into Hinge Health. This team owns our member-to-member referral program end-to-end — the incentive mechanics, referral flows, and personalization systems that motivate existing members to refer colleagues and family members.
This is a high-ownership, full-stack role. You’ll set technical direction, work closely with product and data science, and have a direct line to business outcomes like referral conversion, cost-per-acquisition, and incremental bookings.
WHAT YOU’LL DO
- Own the technical direction and roadmap for the member referral platform across the full stack — from frontend experiences to backend services and data pipelines.
- Scale incentive and referral systems to handle growing member volume and complexity without degrading conversion or user experience.
- Integrate ML model outputs (for example, propensity scores, personalized offers, and targeting signals) into referral surfaces to improve effectiveness.
- Design, instrument, and analyze A/B experiments; work with data science to close the feedback loop between model outputs and product outcomes.
- Partner with product, design, and analytics to define what to build and why, balancing short-term wins with long-term platform leverage.
- Raise the engineering bar by driving code quality, system design standards, observability, and technical decision-making on the team.
WHAT WE’RE LOOKING FOR
- Incentive & growth experience — a track record of designing or evolving systems that measurably shift user behavior through incentives, rewards, or social mechanics. Domain background matters less than demonstrated growth impact.
- Personalization at scale — experience operationalizing ML model pipelines, model serving, A/B testing infrastructure, and feedback loops that connect model performance to product outcomes.
- Full-stack depth — strong skills across technologies such as React, React Native, TypeScript, NestJS, GraphQL, Node.js, relational and non-relational databases, Docker, Kubernetes, AWS, and Redis. We don’t expect a perfect match on every technology, but you should be fluent in most.
- Staff-level leadership — you’ve set technical direction for a team, influenced cross-functional roadmaps, and made architectural decisions that have held up over time.
BASIC QUALIFICATIONS
- Bachelor’s degree (or foreign equivalent) in Computer Science, Engineering, or a related technical field.
- 4+ years of professional software engineering experience, including meaningful time working on production systems at scale.
- Hands-on experience building and operating full-stack applications (for example, using React/React Native on the frontend and Node.js/TypeScript or similar on the backend).
PREFERRED QUALIFICATIONS
- Experience designing and scaling incentive, rewards, or referral systems that drive measurable user and business outcomes.
- Experience integrating ML models into production systems (for example, model serving, feature pipelines, experimentation, and feedback loops).
- Depth with modern web and service technologies such as React, React Native, TypeScript, NestJS, GraphQL, Node.js, relational and non-relational databases, Docker, Kubernetes, AWS, and Redis.
- Proven experience setting technical direction for a team, owning architectural decisions, and driving high-quality execution in partnership with product and data science.
- Experience with experimentation platforms, A/B testing frameworks, or ML feature stores.
- Prior work at a consumer growth-focused company (for example, e-commerce, marketplaces, or consumer apps) and/or experience with viral or referral growth mechanics.
- Experience working in healthcare or other regulated-data environments.
WHY THIS ROLE
- High ownership, low bureaucracy — this team moves quickly and ships iteratively while maintaining high technical
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