Research Product Manager – AI Systems

Research Product Manager — AI Systems (Structured Data, Evaluation & Learning Efficiency) About the Role We’re hiring a Research Product Manager to define and build core systems that determine how AI models are evaluated, improved, and deployed on real-world data. You’ll work on systems spanning: model evaluation and benchmarking post-training and feedback loops structured and relational data learning performance, efficiency, and cost optimization This role sits at the intersection of ML infrastructure, research, and product. It is closest to roles like ML platform PM or AI infrastructure PM, but with deeper ownership of how systems are designed and how model performance translates into real-world outcomes. You’ll partner closely with researchers and engineers to move ideas from experiments into production systems used at scale. The Mission AI today is no longer bottlenecked by model architecture alone. The real constraints are: how models are evaluated how they improve after training how they behave in real-world systems Granica is building the systems that solve this. We are a research and systems company led by Prof. Andrea Montanari (Stanford), focused on: evaluation as a first-class system post-training as a continuous learning loop efficient learning over real-world data Most real-world data is structured and relational, yet modern AI systems remain poorly optimized to learn from it. Our thesis: AI advantage will come from how efficiently models learn from structured data—and how that translates into economic value. What You’ll Do Define and drive systems for model evaluation, benchmarking, and real-world performance Build product direction for post-training systems and feedback loops that continuously improve models Define how models learn from large-scale structured and relational datasets Partner with engineering to build systems that connect data platforms (warehouses, lakehouses) with ML systems Own how improvements move from research experiments into production systems Model trade-offs across compute, data efficiency, performance, and cost Identify where system improvements drive measurable business impact Skills and QualificationsMinimum Qualificati ons 5+ years of experience in product management, technical program management, or similar roles in AI, ML infrastructure, or data systems Strong understanding of machine learning systems, including training, evaluation, and deployment Experience working with large-scale data systems or distributed infrastructure Ability to reason about trade-offs across data, compute, performance, and cost Track record of driving complex technical systems from concept to production Preferred Qualifications Experience with ML platforms, LLM systems, or AI infrastructure Experience with evaluation systems, observability, or model performance tooling Familiarity with structured or relational data systems (e.g.