AI Fullstack Engineer, Health Intelligence

Role Overview

• Work with rich longitudinal signals from wearables plus real-world context.
• Help turn ambiguous health questions into structured, testable AI systems.
• Balance scientific depth, engineering pragmatism, and product impact so millions of members get guidance that respects both their biology and their real lives.
• Design and build LLM‑backed product capabilities: Ship user-facing features that use LLMs and other AI models to deliver personalized insights, guidance, and proactive notifications. Implement safe tool-calling, retrieval, and orchestration so that AI components behave deterministically where they must and adaptively where they can.
• Own evaluation, quality, and safety for AI workflows: Lead the design and implementation of evaluation frameworks and tooling to measure quality, safety, latency, and cost before and after release. Define the metrics and slices that matter for user-facing guidance, and integrate evals into the production pipeline.
• Integrate LLMs with personalization and understanding layers: Ground AI behavior in structured user context rather than one-off prompts. Connect AI components to navigation flows, product surfaces, and action systems so guidance turns into coherent, multi-step programs and one-tap actions, not isolated tips.
• Contribute to a multi-LLM and reasoning platform: Prototype and productionize workflows across multiple model providers and configurations, including routing logic and shadow-mode experimentation. Collaborate with infrastructure and science teams on reasoning, planning, and multimodal use cases.
• Build robust, observable, and cost-aware systems: Design and implement services, workflows, and full stack product capabilities that meet reliability and performance expectations. Take ownership of operational health: debugging production issues, reducing technical debt, and iterating on architecture as the AI surface area and traffic grow.
• Partner cross-functionally: Work closely with product, data science, research, design, and content to shape problem definitions, constraints, and evaluation plans. Communicate trade-offs clearly and help the team make principled decisions in a fast-moving domain.

Requirements

• 2+ years of hands-on experience in AI engineering, with a multi-year background in backend engineering, full stack product engineering, applied ML, or related roles building production systems.
• Strong proficiency building production systems across the stack, including user-facing product surfaces and backend services, and comfort working with cloud-native services to ship and maintain production features.
• Demonstrated ability to own systems end-to-end: from problem framing and data pipelines through modeling and prompting, all the way to deployment, monitoring, iteration, and product delivery.
• A track record of working in product-facing teams, shipping to real users rather than only research prototypes, and caring about impact and iteration speed.
• Comfort operating in a fast-changing AI/LLM domain with ambiguity, balancing rigor with pragmatism and keeping member value and safety at the center.
• Excellent communication and collaboration skills, including the ability to explain complex technical trade-offs to non-technical stakeholders and work effectively in cross-functional teams across time zones.

Tech Stack

• Cloud

Benefits

• Competitive salary
• Lunch benefit
• Wellness benefit
• Flexible working hours
• Collaborative, smart teammates
• An Oura ring of your own
• Wellness Time Off