---
title: 'Product Manager – Large Tabular Models at Granica'
canonical: 'https://feeny.ai/job/product-manager-large-tabular-models-granica-bay-area-dmfh4stjx4qb'
type: 'job'
last_seen: '2026-11-10'
---

# Product Manager – Large Tabular Models at Granica

- **Company:** Granica
- **Location:** Bay Area
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-11-07
- **Last confirmed live:** 2026-11-10
- **Apply:** https://jobs.ashbyhq.com/granica/bc159dc0-98eb-4e63-a750-52ba975186ec

## Job description

Product Manager – Large Tabular Models 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 Product Manager to turn Large Tabular Models into products that solve important enterprise problems. This is a hands-on product role for someone with strong product judgment and unusual technical depth. You’ll work closely with researchers and engineers, but your job is fundamentally product: identify the right problems, decide what to build, test hypotheses, learn from users, and turn emerging model capabilities into products customers adopt and pay for. Experience with structured or tabular data is valuable, but not required. We are equally interested in exceptional PMs from AI products, search/retrieval, recommendation, ML platforms, developer tools, data infrastructure, and analytics.

The Mission Most valuable enterprise data is structured, relational, private, and constantly changing. Granica is building models that learn the underlying structure of enterprise data deeply enough to support many capabilities — including prediction, forecasting, anomaly detection, classification, imputation, synthetic data, and risk modeling. The goal is to move beyond one model per task.

## What You’ll Do

- Own product direction, roadmap, prioritization, and execution for Large Tabular Model products
- Identify high-value customer problems where LTM capabilities create a meaningfully better solution
- Work directly with customers to understand workflows, pain points, and adoption barriers
- Partner with researchers and engineers to turn emerging model capabilities into product requirements and experiments
- Build feedback loops across customer usage, product metrics, evaluations, and model improvements
- Drive products from idea → prototype → launch → iteration
- Translate technical improvements into measurable customer and economic value

What Makes This Problem Different Structured enterprise data requires models to understand schemas, relationships, heterogeneous data types, distributions, temporal behavior, and business context. The product challenge is determining which capabilities matter, for which users, and whether they are reliable enough to create real value.

Evaluation Is a Core Part of the Product A benchmark score alone cannot tell us whether a model or product is useful. We care about reliability, failure modes, generalization, real-world usage, and customer value. Evaluation is a core feedback mechanism for deciding what to build, improve, and launch.

## Skills and Qualifications

## Minimum Qualifications

- 5+ years of hands-on Product Management or equivalent product ownership
- Track record of taking technically complex products from ambiguous problem to launch and iteration; ideally partnering with science / R&D organizations
- Strong technical judgment and ability to work directly with researchers and engineers
- Strong product judgment and customer orientation
- Experience using metrics, experiments, evaluations, or product telemetry to guide decisions
- Comfort operating in a highly technical 0→1 environment
- Experience with AI/ML products, search/retrieval, developer platforms, data infrastructure, analytics, or other technically complex systems

Especially Valuable

- Experience with structured, relational, or tabular data
- Experience building products on top of foundation models or other ML systems
- Background in engineering, ML, data science, or another technical discipline
- Ability to prototype directly using code, data, APIs, or AI tools
- Strong enterprise and commercial instincts

Ideal Backgrounds We are especially interested in hands-on PMs from:

- AI products and model-powered applications
- Snowflake, Databricks, AWS, Google Cloud, Microsoft, or similar data platforms
- OpenAI, Anthropic, Google, Meta, or similar AI organizations
- Search, recommendation, ranking, forecasting, risk, fraud, or decision systems
- Developer platforms, ML infrastructure, and technically complex APIs

We care much more about what you personally built and owned than your title.

## Why This Role Matters

Granica believes the next major enterprise AI breakthrough will come from learning much more deeply from the structured data that runs businesses. This role will help determine which problems are worth solving, what the product becomes, how we know it works, and where emerging model capability creates real customer value.

## 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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