Research Product Manager – AI Systems
Granica · Bay Area Office
Extracted from the posting
Research Product Manager defining product direction for AI systems that learn from structured and relational enterprise data. The role works directly with researchers and engineers to define model evaluation, move research into production, and connect systems to data platforms, warehouses, and lakehouses. No specific stack is listed.
Research Product Manager – AI Systems
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 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 infrastructuremodel evaluation, training, post-training, inference, or experimentationstructured / tabular MLdatabases, warehouses, lakehouses, or large-scale data platformsapplied 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.
Compensation: $160K - $240K
Company stack
This posting doesn’t list technologies. These are the ones Granica uses across its other roles.
