Ōura
Health Intelligence
SeniorAIEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior AI Engineer at Ōura”
What You'll Achieve.
Deliver personalized insights; Deliver guidance; Deliver proactive notifications; AI components behave deterministically; AI components adaptively; Measure quality; Measure safety; Measure latency; Measure cost; Guidance respects biology; Guidance respects real lives; Guidance turns into coherent programs; Guidance turns into one-tap actions; Meet reliability expectations; Meet performance expectations; Reduce technical debt; Iterate on architecture; Shape problem definitions; Shape constraints; Shape evaluation plans; Make principled decisions
Industry & Context.
Root cause analysis; Debugging; Troubleshooting
What They're Looking For.
Must Have
2+ years AI engineering experience, Multi-year backend engineering background, Multi-year applied ML background, Experience building production systems, Proficiency in backend or ML language, Comfort with cloud-native services, Own systems end-to-end, Experience in product-facing teams, Shipped to real users, Comfort operating in fast-changing AI/LLM domain, Balance rigor with pragmatism, Excellent communication skills, Excellent collaboration skills
Nice to Have
LLM evaluation experience, LLM tooling experience, LLM-as-judge experience, Rubric-based scoring experience, Red-teaming experience, Prompt versioning experience, Evaluation platforms experience, RAG familiarity, Knowledge graphs familiarity, Semantic retrieval systems familiarity, Personalization systems background, Recommendation systems background, Ranking systems background, Digital health exposure, Wearables exposure, Behavior change exposure, Habit formation interest, Developer tooling experience, Experimentation frameworks experience, Analytics products experience, Observability products experience, Experience in distributed teams
How You'll Work.
Team & Collaboration
Cross-functional teams; Product teams; Data science teams; Research teams; Design teams; Content teams; Infrastructure teams; Science teams; Cross-functional teams across time zones
Communication Scope
Explain technical trade-offs
Full Job Description
Our mission at Oura is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. 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. As a Senior AI Engineer, you will design, build, and operate the systems that make this possible: LLM-backed workflows, retrieval and knowledge representations, evaluation pipelines, and personalization logic that together power personalized guidance, proactive engagement, and new AI-driven product experiences. You’ll: 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. This role is ideal for someone who wants to work end-to-end - from problem framing and model/tooling choices to productionization, evaluation, and iteration - in a domain where outcomes and behavior cha
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