Employment Type: Full-time
Work Model: On-site, 5 days/week
Department: Research
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 RoleGranica 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 DoBuild 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
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
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
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
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.
Skills Required
- Strong engineering depth in distributed systems, data processing systems, query engines, databases, or cloud infrastructure
- Production experience with distributed compute or query systems such as Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, EMR, Glue, or Hive
- Hands-on experience improving performance, reliability, or cost efficiency for large-scale data-processing workloads
- Understanding of distributed execution, query planning, scheduling, resource management, fault tolerance, and workload isolation
- Experience with Spark internals, Spark SQL, Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling
- Familiarity with lakehouse formats and columnar data such as Iceberg, Delta Lake, Hudi, Parquet, or ORC
- Familiarity with cloud object storage systems such as S3, GCS, or ADLS and their distributed-compute performance tradeoffs
- Strong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languages
- Curiosity about workload optimization, cost modeling, adaptive execution, and compute efficiency for AI and analytics at scale
- Pragmatic builder mindset with rigorous, hands-on ownership of complex systems end to end
- Experience contributing to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems
- Experience with cost-based optimization, query planning, vectorized execution, or distributed runtime systems
- Experience optimizing joins, aggregations, shuffles, scans, spills, caching, partitioning, skew handling, or task scheduling
- Experience building workload schedulers, execution control planes, resource managers, or multi-engine compute platforms
- Experience reducing compute cost or improving workload efficiency in large-scale production data environments
- Background in query engines, distributed runtimes, storage-aware execution, indexing, caching, encoding, compression, or adaptive query optimization
- Research or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure
What We Do
Our mission is to remove inefficiency from the foundation of AI. By combining new research in information theory, probabilistic modeling, and distributed systems, we’re creating self-optimizing data infrastructure that continuously improves how information is represented and used by intelligent systems.
Why Work With Us
We’re a tight-knit team combining --> * Fundamental research in compression, data systems, and information theory * World-class systems engineering across storage, infrastructure, and research led by our Chief Scientist & Stanford Prof. Andrea Montanari * A shared obsession with performance, scale, and clean design
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