---
title: 'Research Product Manager – AI Systems at Granica'
canonical: 'https://feeny.ai/job/research-product-manager-ai-systems-granica-bay-area-dmfh4stjx4qb'
type: 'job'
last_seen: '2026-09-08'
---

# Research Product Manager – AI Systems at Granica

- **Company:** Granica
- **Location:** Bay Area
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-19
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/granica/bc159dc0-98eb-4e63-a750-52ba975186ec

## Job description

RESEARCH PRODUCT MANAGER — AI SYSTEMS (STRUCTURED DATA, EVALUATION & LEARNING EFFICIENCY)

## ABOUT THE ROLE

We’re hiring a Research Product Manager to define and build core systems that determine how AI models are evaluated, improved, and deployed on real-world data.

You’ll work on systems spanning:

- model evaluation and benchmarking
- post-training and feedback loops
- structured and relational data learning
- performance, efficiency, and cost optimization

This role sits at the intersection of ML infrastructure, research, and product. It is closest to roles like ML platform PM or AI infrastructure PM, but with deeper ownership of how systems are designed and how model performance translates into real-world outcomes.

You’ll partner closely with researchers and engineers to move ideas from experiments into production systems used at scale.

## THE MISSION

AI today is no longer bottlenecked by model architecture alone.

The real constraints are:

- how models are evaluated
- how they improve after training
- how they behave in real-world systems

Granica is building the systems that solve this.

We are a research and systems company led by Prof. Andrea Montanari (Stanford), focused on:

- evaluation as a first-class system
- post-training as a continuous learning loop
- efficient learning over real-world data

Most real-world data is structured and relational, yet modern AI systems remain poorly optimized to learn from it.

Our thesis: AI advantage will come from how efficiently models learn from structured data—and how that translates into economic value.

## WHAT YOU’LL DO

- Define and drive systems for model evaluation, benchmarking, and real-world performance
- Build product direction for post-training systems and feedback loops that continuously improve models
- Define how models learn from large-scale structured and relational datasets
- Partner with engineering to build systems that connect data platforms (warehouses, lakehouses) with ML systems
- Own how improvements move from research experiments into production systems
- Model trade-offs across compute, data efficiency, performance, and cost
- Identify where system improvements drive measurable business impact

## SKILLS AND QUALIFICATIONS

## MINIMUM QUALIFICATIONS

- 5+ years of experience in product management, technical program management, or similar roles in AI, ML infrastructure, or data systems
- Strong understanding of machine learning systems, including training, evaluation, and deployment
- Experience working with large-scale data systems or distributed infrastructure
- Ability to reason about trade-offs across data, compute, performance, and cost
- Track record of driving complex technical systems from concept to production

## PREFERRED QUALIFICATIONS

- Experience with ML platforms, LLM systems, or AI infrastructure
- Experience with evaluation systems, observability, or model performance tooling
- Familiarity with structured or relational data systems (e.g., warehouses, lakehouses)
- Background in engineering, applied research, or ML systems development
- Experience operating in research-driven or highly ambiguous environments

## IDEAL BACKGROUNDS

- ML / AI infrastructure PMs (OpenAI, Google, Meta, Snowflake, Databricks, AWS, or similar)
- Product leaders in model systems, evaluation, or observability
- Research engineers or applied scientists transitioning into product
- Engineers who have built ML or data systems and taken on product ownership

## WHY THIS ROLE MATTERS

Most AI systems are limited not by model capability, but by:

- weak evaluation systems
- inefficient learning loops
- poor utilization of structured data
- lack of connection between performance and real-world outcomes

This role defines how those constraints are solved in production systems.

You won’t be optimizing features—you’ll be defining the systems that determine how models improve, how they are trusted, and how they deliver value.

## LOGISTICS

- Location: Mountain View, CA
- Work model: On-site, five days per week
- Level: Senior / Staff / Principal (depending on experience)

## 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)

## Other roles at Granica

- [Senior Software Engineer — Distributed Compute / Spark Systems](https://feeny.ai/job/senior-software-engineer-distributed-compute-spark-systems-granica-bay-area-7k9r7jezn5t1) — Bay Area
- [Senior Software Engineer — Lakehouse Systems](https://feeny.ai/job/senior-software-engineer-lakehouse-systems-granica-bay-area-4apk25mqqrb0) — Bay Area
- [Forward Deployed Engineer](https://feeny.ai/job/forward-deployed-engineer-granica-bay-area-3mqk9yj6gyez) — Bay Area
- [Enterprise Account Executive - Mountain View, onsite](https://feeny.ai/job/enterprise-account-executive-mountain-view-onsite-granica-bay-area-v1mcbe10jx24) — Bay Area
- [Enterprise Account Executive — New York Metro, remote](https://feeny.ai/job/enterprise-account-executive-new-york-metro-remote-granica-new-york-9qt45q7e6n7g) — New York, NY
- [Research Scientist – Diffusion Models](https://feeny.ai/job/research-scientist-diffusion-models-granica-bay-area-g1gcawhdzsns) — Bay Area
- [Research Scientist – Large Tabular Models (LTMs)](https://feeny.ai/job/research-scientist-large-tabular-models-ltms-granica-bay-area-2qa1k66qnecd) — Bay Area
- [Head of Finance — Strategic Finance & Corporate Development](https://feeny.ai/job/head-of-finance-strategic-finance-corporate-development-granica-bay-area-77nfgrymjsbe) — Bay Area
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