Research Product Manager – AI Systems

About Granica

Granica is building the efficiency and intelligence layer for enterprise AI.

• Crunch makes massive enterprise data cheaper and easier to operate.
• Large Tabular Models learn from structured data to support shared intelligence across many capabilities.
• Myelin makes long-running AI agents more efficient and durable.

Granica has processed hundreds of petabytes of tabular data in production, and our research is led by Stanford Professor Andrea Montanari.

Logistics

• Location: Mountain View, CA
• Work model: On-site, five days per week
• Level: Senior / Staff / Principal

About the Role

Granica is hiring a Research Product Manager to turn frontier AI research into systems that create real value from enterprise data.

You’ll work at the intersection of 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.

The Mission

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.

What You’ll Do

• 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.

Models must understand:

• schemas and metadata
• joins and relationships
• heterogeneous data types
• distributions and missingness
• temporal behavior
• business-specific context

The goal is to build models that understand enterprise data deeply enough that many useful capabilities emerge from the same underlying intelligence.

Evaluation Is a Core Part of the Product

A benchmark score alone cannot tell us whether a model has truly learned the structure of enterprise data.

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.

Skills and Qualifications

Minimum Qualifications

• 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

• Experience with 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

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

Why This Role Matters

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.

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

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.