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Granica in Mountain View, CA is hiring a Product Manager to turn Large Tabular Models into enterprise-ready products. You will identify critical problems, define what to build, test hypotheses with users, and convert emerging model capabilities into commercial offerings customers adopt and pay for.
You will work closely with researchers and engineers, leveraging deep technical depth and strong product judgment to guide development from idea to prototype, launch, and continuous iteration in a
Employment Type: Full-time
Work Model: On-site, 5 days/week
Department: Research
Granica helps enterprises run AI for less, from the data layer to agent execution.
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.
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.
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.
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.
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.
We care much more about what you personally built and owned than your title.
Granica believes the next major enterprise AI breakthrough will come from learning much more deeply from the structured data that runs businesses.
This role will help determine which problems are worth solving, what the product becomes, how we know it works, and where emerging model capability creates real customer value.
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.