---
title: 'Senior Software Engineer — Distributed Compute / Spark Systems at Granica'
canonical: 'https://feeny.ai/job/senior-software-engineer-distributed-compute-spark-systems-granica-san-francisco-7k9r7jezn5t1'
type: 'job'
last_seen: '2026-10-20'
---

# Senior Software Engineer — Distributed Compute / Spark Systems at Granica

- **Company:** Granica
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/granica/68e0b1db-0f03-4ad3-85e9-586aeb46f6c2

## Job description

Senior Software Engineer — Distributed Compute / Spark Systems Location: Mountain View, CA — On-site

## About Granica

Granica builds AI infrastructure for enterprises operating massive data environments. Our platform helps data and engineering teams reduce storage and compute costs, improve performance and reliability, and prepare large datasets for analytics and AI.

Granica’s products include:

- Crunch — continuous optimization for enterprise lakehouse data
- Myelin — stateful infrastructure for long-running AI agents
- Large Tabular Models — foundation models designed for enterprise tables

Together, we are building the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.

Granica has demonstrated approximately $200K in annualized value per petabyte and verified customer value within weeks.

## About the Role

Granica is hiring a Senior Software Engineer to build distributed compute systems for enterprise-scale data and AI workloads.

You will work on the core infrastructure behind Crunch, Granica’s continuous optimization product for enterprise lakehouse data. This includes systems for distributed execution, workload optimization, query performance, scheduling, resource management, and compute cost reduction across petabyte- and exabyte-scale environments.

You will own core systems that directly affect customer compute spend, query latency, workload reliability, cluster efficiency, and the performance of large-scale analytical data processing.

This is a hands-on systems role for engineers who have gone deep on distributed execution — particularly Apache Spark or similar query/runtime systems — and want to own performance, scheduling, reliability, and compute efficiency at massive scale.

## What You’ll Do

- Build and optimize distributed compute infrastructure for large-scale analytical and AI workloads
- Improve execution performance across scans, joins, aggregations, shuffles, spills, caching, partitioning, and task scheduling
- Design adaptive systems for workload routing, execution planning, resource allocation, and cluster efficiency
- Debug bottlenecks across CPU, memory, network, storage, metadata, and distributed execution layers
- Improve reliability, fault tolerance, and compute efficiency across Spark and adjacent systems such as Trino, Presto, Flink, Iceberg, Delta Lake, Hudi, and cloud object stores

## What We’re Looking For

- Deep engineering experience in distributed systems, query engines, databases, or cloud infrastructure
- Production experience building, extending, or deeply optimizing distributed compute or query systems such as Spark, Spark SQL, Trino, Presto, Flink, or similar execution engines
- Strong understanding of distributed execution, query planning, scheduling, resource management, fault tolerance, workload isolation, and performance optimization
- Hands-on experience with Spark internals such as Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling
- Strong programming skills in Scala, Java, Go, Rust, C++, or a similar systems-oriented language, with a pragmatic end-to-end builder mindset

Bonus

- Contributions to Spark, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems
- Experience with cost-based optimization, vectorized execution, workload schedulers, execution control planes, resource managers, storage-aware execution, or adaptive query optimization
- Research or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure

## Why Join Granica

- Build foundational infrastructure for enterprise data and AI
- Work on deep systems problems across distributed compute, query execution, scheduling, and compute efficiency
- Own meaningful parts of the architecture in a small, high-caliber engineering team
- Work directly with Product, Engineering, and company leadership
- Have direct impact on customer performance, infrastructure cost, product direction, and company growth

## 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
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