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 .