Self-optimizing compression for petabyte-scale AI data
Granica builds self-optimizing lossless compression that reduces petabyte-scale data to terabytes, halving LLM token usage and query costs. It delivers a new class of adaptive data infrastructure for AI, turning lakehouses into self-improving factories compatible with Iceberg, Delta, Snowflake, Databricks and others. The company focuses on reliable, steerable representations for enterprise structured and tabular data.
Define and build AI evaluation, post-training, and structured data learning systems that improve model performance in production. Partner with researchers and engineers to translate experiments into scalable ML infrastructure.
Conduct research and build foundational ML models for structured and tabular data, combining statistical learning theory, probabilistic modeling, and large-scale systems. Requires a PhD and strong experience in tabular/relational ML.
Lead a globally distributed team of 15-20 senior engineers building core data infrastructure for AI workloads. Hands-on technical leader owning table formats (Iceberg, Delta Lake), distributed systems design, and petabyte-scale data systems.
Drive full-cycle enterprise sales of AI data infrastructure platform to large organizations, building relationships with data and engineering leaders and closing complex multi-year deals.
Drive adoption of Granica's AI data infrastructure platform by owning full enterprise sales cycles with data, infrastructure, and engineering teams. Requires 6+ years of quota-carrying enterprise software sales experience and success closing complex technical deals.
Founding Head of Finance to lead capital strategy, FP&A, and performance systems for a high-growth AI infrastructure startup. Requires experience raising $50M+ rounds and scaling from Series B to IPO.
Lead architecture of exabyte-scale distributed data systems that self-optimize via compression, metadata, and intelligent layouts to power efficient AI infrastructure. Requires deep expertise in distributed systems, low-level data representation, and leadership of large-scale production systems.
Build foundational data systems for AI at scale, including global metadata, adaptive engines, and intelligent data layouts using distributed systems, columnar formats, and languages like Java/Rust/Go/C++.