9 months ago
Base Salary
$190k - $250k/yr
Responsibilities
- Architect a transactional and metadata substrate supporting time-travel, schema evolution, and atomic consistency across petabyte-scale tabular datasets.
- Build adaptive systems that autonomously reorganize data based on access patterns and workloads.
- Optimize data encoding, compression, and layout to maximize signal per byte read.
- Develop distributed compute pipelines that scale predictively, adapt to dynamic load, and remain reliable under failure.
- Implement algorithms in compression, representation, and optimization emerging from ongoing research.
- Design systems that minimize latency between questions and insights.
Requirements
- Deep experience with distributed systems, including consensus, partitioning, replication, and fault tolerance.
- Experience with columnar formats such as Parquet or ORC and low-level encoding strategies.
- Understanding of metadata-driven architectures and adaptive query planning.
- Production experience with Spark, Flink, or custom distributed engines on cloud object storage.
- Proficiency in Java, Rust, Go, or C++ with an emphasis on clarity and quality.
- Curiosity about the mathematics of compression, entropy, and learning efficiency.
- Familiarity with Iceberg, Delta Lake, or Hudi is a bonus.
- Research or open-source contributions in compression, indexing, or distributed computation are a bonus.
- Interest in how data representation affects training dynamics and model reasoning efficiency is a bonus.
Benefits
- Competitive salary, meaningful equity, and substantial bonus for top performers.
- Flexible time off and comprehensive health coverage for the employee and family.
- Support for research, publication, and deep technical exploration.
About Granica
Granica AI is an AI research and products company building generalized intelligence for enterprise: exabyte-scale data infrastructure, generative models for large-scale tabular data, and stateful agent infrastructure. Our focus is efficiency and trust across every layer of the stack from raw data to frontier models to the agents that act on them.