Granica is looking to solve the problem of inefficient data systems that limit the potential of AI, by building the infrastructure for a new kind of intelligence that is structured, efficient, and deeply integrated with data.
Requirements
- Foundational understanding of distributed systems: partitioning, replication, and fault tolerance.
- Experience or curiosity with columnar formats such as Parquet or ORC and low-level data encoding.
- Familiarity with metadata-driven architectures or data query planning.
- Exposure to or hands-on use of Spark, Flink, or similar distributed engines on cloud storage.
- Proficiency in Java, Rust, Go, or C++ and commitment to clean, reliable code.
- Curiosity about how compression, entropy, and representation shape system efficiency and learning.
Responsibilities
- Help design and implement the metadata substrate that supports time-travel, schema evolution, and atomic consistency across massive tabular datasets.
- Build components that reorganize data autonomously, learning from access patterns and workloads to maintain efficiency with minimal manual tuning.
- Develop and refine bit-level encodings, compression, and layout strategies to extract maximum signal per byte read.
- Contribute to distributed compute systems that scale predictively and adapt to dynamic load.
- Translate new algorithms in compression and representation from research into production-grade implementations.
- Design and optimize data paths to minimize time between question and insight, enabling faster learning for both models and humans.
Other
- Competitive salary, meaningful equity, and substantial bonus for top performers
- Flexible time off plus comprehensive health coverage for you and your family
- Support for research, publication, and deep technical exploration
- Bachelor's, Master's, or Ph.D. degree in Computer Science or related field
- Ability to work in a high-trust environment with minimal bureaucracy
