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) w...