Research Product Manager – AI Systems

Granica•San Francisco, CA

2d•$160,000 - $240,000•Onsite

About The Position

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, and 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. AI today is no longer bottlenecked by model architecture alone. The real constraints are how models are evaluated, how they improve after training, and 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, and 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 is that AI advantage will come from how efficiently models learn from structured data—and how that translates into economic value.

Requirements

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

Nice To Haves

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., warehouses, lakehouses)
Background in engineering, applied research, or ML systems development
Experience operating in research-driven or highly ambiguous environments

Responsibilities

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