Senior Software Engineer — Distributed Compute / Spark Systems

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 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