Product Manager – Large Tabular Models

Research Product Manager – AI Systems

About Granica

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

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.

Research Product Manager

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

Today, companies typically build machine learning one problem at a time: define a target, prepare data, train a model, deploy it, and repeat for the next problem.

Granica’s research is pioneering a fundamentally better approach.

We are building models that learn the underlying structure and distributions of enterprise data deeply enough that shared intelligence can support many capabilities — including prediction, anomaly detection, classification, forecasting, imputation, synthetic data, and risk modeling.

The goal is to move beyond one model per task

Evaluation Is a Core Part of the Product

• Granica believes the next major enterprise AI breakthrough will come from learning much more deeply from the structured data that actually runs businesses.
• We are building toward a future where enterprises no longer need a separate bespoke model for every capability.
• 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

Responsibilities

• Research
• 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.
• to turn frontier AI research into systems that create real value from enterprise data.

AI/ML systems, structured data, research, and product, helping define

• how models learn from real-world data how model quality and emerging capabilities are evaluated how research becomes production systems how technical improvements translate into economic value
• Experience with structured or tabular data is a major advantage, but we are equally interested in exceptional product leaders from
• AI systems, ML infrastructure, evaluation, training/post-training, and applied ML
• This is not a traditional feature PM role. You’ll work directly with researchers and engineers to turn technically ambitious ideas into products and systems.
• Define product direction for AI systems that learn from structured and relational data
• Partner with researchers to translate new model capabilities into production systems
• Define how model quality and emerging capabilities are evaluated
• Identify enterprise ML problems that can move from task-specific models toward shared intelligence
• Connect AI systems with enterprise data platforms, warehouses, and lakehouses
• Translate model improvements into measurable customer and economic value Drive research from experiment → system → product → customer value
• Shape the roadmap around the highest-value enterprise problems
• What Makes This Problem Different
• Structured enterprise data is fundamentally different from natural-language corpora.
• schemas and metadata
• joins and relationships
• temporal behavior
• The goal is to build models that understand enterprise data deeply enough that many useful capabilities emerge from the same underlying intelligence.

Requirements

• Granica has already processed
• hundreds of petabytes of enterprise data in production for customers including
• heterogeneous data types

distributions and missingness

• A benchmark score alone cannot tell us whether a model has truly learned the structure of enterprise data.
• 5+ years of product leadership or equivalent technical ownership in AI/ML, data systems, infrastructure, or applied research
• Strong technical judgment and ability to work directly with researchers and engineers
• Experience taking complex technical products or systems from concept to production
• Ability to reason about quality, performance, cost, and real-world outcomes Experience in one or more of:
• AI / ML platforms or infrastructure model evaluation, training, post-training, inference, or experimentation structured / tabular ML databases, warehouses, lakehouses, or large-scale data platforms applied ML systems such as recommendation, forecasting, risk, fraud, or ranking
• Especially Valuable
• structured, relational, or tabular data
• Experience translating research into production systems
• Background in engineering, ML, data science, or research
• Experience connecting technical improvements to customer value
• Comfort operating in a research-driven, highly ambiguous 0→1 environment
• Ideal Backgrounds
• Data infrastructure at Snowflake, Databricks, Microsoft, Google Cloud, or similar

Model evaluation, experimentation, or model-quality systems

• Structured-data ML, recommendation, forecasting, risk, fraud, or decision systems
• Research engineering or applied science with meaningful product ownership

Preferred

• AI / ML infrastructure at OpenAI, Google DeepMind, Meta, Anthropic, AWS, or similar

Pay

Competitive salary, meaningful equity, and performance bonus for top performers

Benefits

• 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

Location

Mountain View, CA — Downtown HQ

Work Model

On-site, 5 days/week

We care about

• whether capabilities are reliable how uncertainty is measured which improvements generalize when research is production-ready when better model performance creates real economic value
• Evaluation is part of the product and research system itself.

This role will help define that transition — what the systems become, how they are evaluated, and how they reach production.