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Oura is seeking a Staff AI Scientist to lead the Health Intelligence team's mission of integrating modern AI and LLMs into the Oura Ring experience. You will own end-to-end development of critical P1 initiatives, bridging classical ML, backend engineering, and generative AI to transform how members interact with their health data.
In this role, you will be hands-on across the full product development lifecycle: from research and data engineering through backend API design, LLM configuration, fine-tuning, retrieval, and evaluation. You will own the development of core intelligent capabilities—researching, building, evaluating, and shipping reusable intelligence systems from scientific signal through product integration and iteration.
Key responsibilities include defining improvements in personalization technology strategy and influencing roadmap direction across partner teams; designing measurement frameworks and evaluation rigor for the intelligent Advisor experience; building lightweight offline evaluation and shadow-mode testing infrastructure to enable rapid iteration; supporting causal and counterfactual model development to distinguish genuine health and behavior outcomes from confounding variables; and mentoring scientists and engineers to raise the bar on experimentation and evaluation practices.
You will collaborate closely with engineering, product, and design teams across US and EU offices, communicating trade-offs, uncertainty, and modeling assumptions to both technical and non-technical stakeholders. This is a high-visibility role for someone who thinks in systems, ships with urgency, and wants to build compounding value over time.
Required: 8+ years in applied AI, AI research, or backend engineering. MS or PhD in Computer Science, Statistics, or related quantitative field strongly preferred. Deep experience shipping LLM-backed products with evaluation workflows (LLM-as-judge, rubric-based evaluation, safety/red-teaming, offline vs. online assessment). Demonstrated ability to build and own systems serving millions of users. Hands-on experience with retrieval, ranking, and recommendation systems (collaborative filtering, embeddings, graph networks). Comfort with server/app engineers on model serving, pipeline architecture, and deployment infrastructure.
