Engineering Manager – Health Intelligence

Oura’s engineering organization consists of talented developers distributed across the EU and US. For day-to-day feature work, our engineers are organized into smaller cross-functional teams. Our teams have a great deal of autonomy and are responsible for the design, development and architecture of their features. Teams take full ownership of their code and handle everything from concepting, design and implementation to release, maintenance and bug fixes. 

The Health Intelligence team is at the forefront of integrating modern AI and LLMs into the Oura experience, transforming how members interact with and learn from their data. We are building the next generation of AI-powered health guidance at Oura, blending traditional ML with modern LLMs, reasoning systems, and robust evaluation - not as “chatbots with vibes,” but as rigorously evaluated components that explain decisions, surface trade-offs, and adapt member journeys over months and years.

We’re looking for an Engineering Manager with a strong backend or data science background who is already acting as a team lead or de‑facto lead and wants to grow on the managerial track while still staying partially hands-on.

In this role you will:

• Lead a multi-disciplinary group of Senior AI Engineers and Senior AI Scientists working on LLM-backed workflows, evaluation pipelines, personalization logic, and intervention models.
• Split your time between people leadership and delivery (majority) and hands-on contributions (e.g., technical design reviews, code or experiment work, prototyping, debugging) where it helps unblock the team or de‑risk a problem.
• Partner closely with product, data, and science leadership to ensure we build AI that is grounded in data, constrained by guardrails, and measured by outcomes and behavior change – not just engagement metrics.

This is a great fit for someone who has been a tech lead / lead engineer / lead data scientist, enjoys coaching others and coordinating complex work, and now wants to formalize that into an Engineering Manager role without giving up all contact with the code and models.

You don’t need to do all of these on day one, but these are the kinds of problems you’ll own:

• →Lead and grow a high-performing AI engineering team: Manage, coach, and develop a group of AI Engineers and Scientists working on LLMs, applied ML, evaluation, and behavior/health modeling.
• →Act as a “player‑coach” for AI initiatives: Focus primarily on people leadership and coordination while occasionally contributing hands-on to design reviews, code/experiment work, and prototyping to de‑risk and unblock.
• →Own delivery for critical AI work in Health Intelligence: Drive planning, scoping, and execution for Advisor, Adaptive Insights, notifications, and related AI features with clear milestones and predictable delivery.
• →Create a strong partnership model across product, science, and engineering: Align people around shared problem definitions, success metrics, and evaluation strategies.
• →Provide technical leadership and guardrails: Stay close enough to the technical and scientific work to support sound decisions and guide trade-offs between complexity, cost, latency, and long-term maintainability.
• →Shape how we evaluate and operate AI systems in production: Ensure robust evaluation, monitoring, incident response, and continuous improvement practices for LLM- and ML-powered workflows.
• →Foster healthy, sustainable ways of working: Build a team culture that balances ambition with sustainability, clear priorities, psychological safety, and a growth mindset.

We’d love to hear from you if you have:

• 5+ years of experience as a backend engineer or data scientist shipping production systems used by real users.
• 1–3 years as a tech lead or de‑facto lead coordinating work and mentoring others, with a clear interest in moving into formal people management.
• Practical exposure to ML or LLM-powered systems and comfort working closely with scientists and ML engineers on real product problems.
• Demonstrated ability to own cross-functional projects end‑to‑end from problem framing through rollout and follow-up iteration.
• Strong written and verbal communication skills, able to align engineers, scientists, and product partners and explain trade-offs clearly.
• Desire to spend most time on people leadership and coordination while keeping a smaller, well-defined slice for high-leverage hands-on work.
• Comfort operating in a fast-changing AI/LLM landscape, learning continuously, and keeping member safety and value at the center.