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 Qualifications
Minimum Qualifications
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., warehouses, lakehouses)
Background in engineering, applied research, or ML systems development
Experience operating in research-driven or highly ambiguous environments
Ideal Backgrounds
ML / AI infrastructure PMs (OpenAI, Google, Meta, Snowflake, Databricks, AWS, or similar)
Product leaders in model systems, evaluation, or observability
Research engineers or applied scientists transitioning into product
Engineers who have built ML or data systems and taken on product ownership
Why This Role Matters
Most AI systems are limited not by model capability, but by:
weak evaluation systems
inefficient learning loops
poor utilization of structured data
lack of connection between performance and real-world outcomes
This role defines how those constraints are solved in production systems.
You won’t be optimizing features-you’ll be defining the systems that determine how models improve, how they are trusted, and how they deliver value.
Logistics
Location: Mountain View, CA
Work model: On-site, five days per week
Level: Senior / Staff / Principal (depending on experience)
Compensation & Benefits
Competitive salary, meaningful equity, and performance bonus for top performers
401(k) with company match, comprehensive health coverage, and unlimited PTO
Daily catered meals in our Mountain View office
Support for research, publication, and conference participation
At Granica, you'll help build the next generation of enterprise AI -from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.