---
title: 'Research Product Manager – AI Systems at Granica'
canonical: 'https://feeny.ai/job/research-product-manager-ai-systems-granica-san-francisco-dmfh4stjx4qb'
type: 'job'
last_seen: '2026-10-20'
---

# Research Product Manager – AI Systems at Granica

- **Company:** Granica
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-14
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/granica/bc159dc0-98eb-4e63-a750-52ba975186ec

## Job description

## 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.

## About Granica

## Company Overview
- **One-liner**: Granica builds self-optimizing data infrastructure that compresses enterprise tabular data and enables structured intelligence for AI workloads.
- **Entity Type**: Private (Series A)
- **Headquarters**: Mountain View, California, United States
- **Founded**: 2023
- **Founders**: Not publicly available

## Core Business
- Primary industry/industries: AI Infrastructure, Data Compression, Research Services
- Target customers: Enterprise B2B — SaaS, consumer-internet, healthcare, and transportation companies with petabyte-scale data estates
- Mission or purpose statement: "Turning entropy to intelligence" — building a new class of data infrastructure that makes data estates efficient, reliable, and steerable for AI

## Products & Services
- **[Crunch]**: A self-optimizing, lossless compression layer for structured data (Iceberg, Delta, Trino, Spark, Snowflake, BigQuery, Databricks). Reduces storage by 45–80% and cuts cloud query spend by 15–35%. Deploys inside a customer's VPC with zero code changes and zero downtime. Continuously adapts to query patterns and data drift.
- **[EΣL (Extract, Signify, Load)]**: A reimagining of ETL. During "Signify," the system learns distributions, keys, and temporal drift while storing data, enabling real-time inference over a latent space without scanning cold blocks.
- **[Large Tabular Models]**: In-development systems that learn cross-column and relational structure to deliver trustworthy answers and automation with provenance and governance.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding — $45.0M (Series A, June 2023)
- **Lead Investor**: New Enterprise Associates (led the $45M Series A, with 6 total investors)
- **Notable Investors/Partners**: New Enterprise Associates, plus 5 other undisclosed institutional investors
- **Growth Signals**: 44.4% headcount growth year-over-year (35 employees), LinkedIn followers up 238.9% yearly, active 9 open job postings, deployments ranging from 1 PB to 100+ PB across dozens of enterprise customers

## Competitive Advantages
- **Entropy-aware compression**: Delivers state-of-the-art compression ratios (45–80% byte reduction) that are continuously adaptive to query patterns and data drift, unlike static compression schemes.
- **Zero disruption deployment**: Operates inside the customer's VPC with no code changes, no downtime, and day-zero activation — dashboards show savings before "coffee cools."
- **Research moat**: Foundational research published at NeurIPS 2024 (weighted empirical risk minimization with surrogate data) and ongoing work on statistical theory of data selection under weak supervision. Chief Scientist Andrea Montanari (Stanford) leads the research agenda.
- **Dual value proposition**: Simultaneously reduces storage costs (pennies per GB) and accelerates query latency (petabytes queried like terabytes), while also optimizing LLM token utilization by up to 50%.

## Strategic Focus
- **Near-term**: Scale Crunch adoption across enterprise data lakes, with a focus on Snowflake, Databricks, and BigQuery ecosystems.
- **Medium-term**: Expand from compression into advanced subsampling and safe synthetic data generation, turning any lake into a "self-optimizing data factory."
- **Long-term**: Build Large Tabular Models that enable real-time reasoning over exabyte-scale data without scanning cold blocks — replacing traditional warehouse scans with inferred answers.

## Why Work Here
- **High-impact engineering culture**: 51% of the team is in technical roles, 18% in research — the company is deeply engineering-first and research-driven. Engineers work on foundational data systems for AI at petabyte scale.
- **Cutting-edge ML research**: Opportunity to work alongside a Chief Scientist from Stanford and publish at top venues (NeurIPS 2024). The company is advancing the state-of-the-art in data compression, subsampling, and synthetic data.
- **Remote/hybrid/office policy**: Headquarters in Mountain View, CA (287 Castro Street). Job postings indicate Mountain View is onsite. Also has offices in India (9 employees) and Austria (1 employee).
- **Notable perks**: "Pays for itself" ROI philosophy — the product delivers measurable cost savings to customers. The company is well-funded ($45M Series A) with strong investor backing.
- **Team composition**: Small, high-leverage team (35 people) with alumni from Meta, Salesforce, Dremio, StackRox, UiPath, and Stanford. Alums go on to LangChain, Google, Rippling, Uber, and Temporal Technologies.
- **Active hiring**: 9 open positions including Senior Software Engineer (Foundational Data Systems), Engineering Manager, Research Scientist (Tabular & Structured ML), Staff Software Engineer, and Research Product Manager.

## Sources
1. [granica.ai](https://www.granica.ai/)
2. [granica.ai/about](https://www.granica.ai/about)
3. [LinkedIn](https://www.linkedin.com/company/granica-ai)
4. [PitchBook](https://pitchbook.com/profiles/company/528930-82)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/granica)

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