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Fraser Township, Michigan, United States
Onsite
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.
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.
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.
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.
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
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.
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.
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
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
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
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.
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.
