Research Product Manager – AI Systems

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.

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.

• 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

• 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

• 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

• 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

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.

• Location: Mountain View, CA

• Work model: On-site, five days per week

• Level: Senior / Staff / Principal (depending on experience)

• 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.