Product Manager – Large Tabular Models

Product Manager – Large Tabular Models

Location: Mountain View, CA — Downtown HQ

Employment Type: Full-time

Work Model: On-site, 5 days/week

Department: Research

About Granica

Granica helps enterprises run AI for less, from the data layer to agent execution.

Granica is building across three related layers:

Crunch reduces storage and processing costs across enterprise data infrastructure.

Large Tabular Models unlock predictions and generated data from governed enterprise tables.

Myelin reduces token and compute overhead for long-running AI agents.

Everything runs inside the customer’s perimeter, so enterprises retain control of their data and the intelligence it creates.

Granica has already processed hundreds of petabytes of enterprise data in production for customers including Snap, Pinterest, Lyft, and ShareChat.

Our research organization is led by Stanford Professor Andrea Montanari.

We operate at the intersection of data infrastructure, AI systems, and applied research, building the efficiency and intelligence layer for enterprise AI.

About the Role

Granica is hiring a Product Manager to turn Large Tabular Models into products that solve important enterprise problems.

This is a hands-on product role for someone with strong product judgment and unusual technical depth. You’ll work closely with researchers and engineers, but your job is fundamentally product: identify the right problems, decide what to build, test hypotheses, learn from users, and turn emerging model capabilities into products customers adopt and pay for.

Experience with structured or tabular data is valuable, but not required. We are equally interested in exceptional PMs from AI products, search/retrieval, recommendation, ML platforms, developer tools, data infrastructure, and analytics.

The Mission

Most valuable enterprise data is structured, relational, private, and constantly changing.

Granica is building models that learn the underlying structure of enterprise data deeply enough to support many capabilities — including prediction, forecasting, anomaly detection, classification, imputation, synthetic data, and risk modeling.

The goal is to move beyond one model per task.

What You’ll Do

Own product direction, roadmap, prioritization, and execution for Large Tabular Model products

Identify high-value customer problems where LTM capabilities create a meaningfully better solution

Work directly with customers to understand workflows, pain points, and adoption barriers

Partner with researchers and engineers to turn emerging model capabilities into product requirements and experiments

Build feedback loops across customer usage, product metrics, evaluations, and model improvements

Drive products from idea → prototype → launch → iteration

Translate technical improvements into measurable customer and economic value

What Makes This Problem Different

Structured enterprise data requires models to understand schemas, relationships, heterogeneous data types, distributions, temporal behavior, and business context.

The product challenge is determining which capabilities matter, for which users, and whether they are reliable enough to create real value.

Evaluation Is a Core Part of the Product

A benchmark score alone cannot tell us whether a model or product is useful.

We care about reliability, failure modes, generalization, real-world usage, and customer value. Evaluation is a core feedback mechanism for deciding what to build, improve, and launch.

Skills and Qualifications

Minimum Qualifications

5+ years of hands-on Product Management or equivalent product ownership

Track record of taking technically complex products from ambiguous problem to launch and iteration; ideally partnering with science / R&D organizations

Strong technical judgment and ability to work directly with researchers and engineers

Strong product judgment and customer orientation

Experience using metrics, experiments, evaluations, or product telemetry to guide decisions

Comfort operating in a highly technical 0→1 environment

Experience with AI/ML products, search/retrieval, developer platforms, data infrastructure, analytics, or other technically complex systems