---
title: 'Forward Deployed Engineer at Granica'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-granica-san-francisco-3mqk9yj6gyez'
type: 'job'
last_seen: '2026-10-20'
---

# Forward Deployed Engineer at Granica

- **Company:** Granica
- **Location:** San Francisco, CA
- **Compensation:** $160k–$220k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-17
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/granica/165a62b6-014d-4d83-a800-a0ff3b205745

## Job description

Location: Mountain View, CA — On-site

## About Granica

Granica is building the efficiency and intelligence layer for enterprise AI. Enterprises already operate enormous proprietary data estates and increasingly build AI systems on top of them, but the underlying infrastructure remains fragmented, expensive, and inefficient.

Granica is building across three related layers:

- Crunch — efficiency for enterprise data
- Large Tabular Models — intelligence for enterprise structured data
- Myelin — efficiency for stateful, long-running AI agents

Crunch continuously optimizes large enterprise lakehouse environments across technologies such as Apache Spark, Iceberg, Delta Lake, Parquet, Snowflake, and Databricks — reducing storage and compute cost while improving data layout and operational efficiency.

Granica has already processed hundreds of petabytes of customer data for companies including Snap, Pinterest, Lyft, and ShareChat, with customers able to validate meaningful savings within weeks.

## About the Role

Granica is hiring a Forward Deployed Engineer to become the technical bridge between our customers, Engineering, Product, and GTM teams. You will work directly inside complex enterprise data environments to understand workloads, investigate performance and cost problems, deploy Granica, run technical evaluations, and turn the results into clear customer recommendations. This is a highly hands-on role. You should be comfortable moving from a customer conversation to Spark debugging, SQL analysis, performance benchmarking, architecture decisions, and an executive-facing explanation of the technical and economic tradeoffs. As one of Granica’s first dedicated Forward Deployed Engineers, you will also help define how the function operates as we scale.

## What You’ll Do

- Lead technical discovery with enterprise data, infrastructure, and AI teams
- Understand customer data architecture, workloads, constraints, and business priorities
- Deploy and configure Granica in complex customer-controlled environments
- Investigate performance, reliability, storage, and compute-cost problems across large data platforms
- Debug and optimize Apache Spark workloads and related distributed-data systems
- Analyze table layouts, file structure, partitioning, compaction, and data-maintenance behavior
- Design and execute technical evaluations and proofs of value with clear success criteria
- Establish trustworthy measurements and translate results into defensible customer recommendations
- Explain technical tradeoffs across performance, cost, reliability, and operational complexity
- Partner with Account Executives during technical discovery, evaluations, and enterprise sales cycles
- Present findings and business value to engineers, architects, and executive stakeholders
- Translate recurring customer problems into Product and Engineering priorities
- Build repeatable deployment, troubleshooting, benchmarking, and evaluation playbooks

## What We’re Looking For

- 5+ years in Forward Deployed Engineering, Data Engineering, Solutions Architecture, Sales Engineering, or a similarly hands-on technical role
- Strong hands-on Apache Spark experience, including production workloads, debugging, performance tuning, and distributed execution behavior
- Strong understanding of distributed data systems and modern cloud data infrastructure
- Experience with lakehouse technologies such as Apache Iceberg, Delta Lake, Parquet, Hive, or comparable systems
- Strong SQL skills and proficiency in Python, Scala, Java, or another relevant programming language
- Experience diagnosing performance or reliability problems using data and experimentation
- Ability to reason about infrastructure economics, including storage, compute, and performance tradeoffs
- Strong customer communication skills and the ability to explain technical recommendations clearly
- High ownership and comfort operating without an established playbook
- Ability to work from our Mountain View office five days per week and travel to customer locations when needed

## Why Join Granica

- Work directly on some of the largest enterprise data environments in production
- Solve technically difficult problems where improvements can translate directly into substantial customer savings
- Work across Spark, lakehouse systems, distributed infrastructure, AI, and emerging enterprise data architectures
- Partner directly with customers, company leadership, Product, Engineering, and GTM
- Help build Granica’s Forward Deployed Engineering function from the ground up
- Influence both product direction and how Granica engages technically with customers
- Join a small team working at the intersection of enterprise data infrastructure and AI

## 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

- [Research Scientist – Diffusion Models](https://feeny.ai/job/research-scientist-diffusion-models-granica-san-francisco-g1gcawhdzsns) — San Francisco, CA
- [Senior Software Engineer — Lakehouse Systems](https://feeny.ai/job/senior-software-engineer-lakehouse-systems-granica-san-francisco-4apk25mqqrb0) — San Francisco, CA
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- [Research Product Manager – AI Systems](https://feeny.ai/job/research-product-manager-ai-systems-granica-san-francisco-dmfh4stjx4qb) — San Francisco, CA
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