AI today is limited not only by model design but by the inefficiency of the data that feeds it. At scale, each redundant byte, each poorly organized dataset, and each inefficient data path slows progress and compounds into enormous cost, latency, and energy waste.
Granica’s mission is to remove that inefficiency. We combine new research in information theory, probabilistic modeling, and distributed systems to design self-optimizing data infrastructure: systems that continuously improve how information is represented and used by AI.
This engineering team partners closely with the Granica Research group led by Prof. Andrea Montanari (Stanford), bridging advances in information theory and learning efficiency with large-scale distributed systems. Together, we share a conviction that the next leap in AI will come from breakthroughs in efficient systems, not just larger models.
What You’ll BuildGlobal Metadata Substrate. Define and evolve the global metadata and transactional substrate that powers atomic consistency and schema evolution across exabyte-scale data systems.
Adaptive Engines. Architect self-optimizing systems that continuously reorganize and compress data based on access patterns, achieving order-of-magnitude efficiency gains.
Intelligent Data Layouts. Pioneer new approaches to encoding and layout that push theoretical limits of signal per byte read.
Autonomous Compute Pipelines. Lead development of distributed compute platforms that scale predictively and maintain reliability under extreme load and failure conditions.
Research to Production. Collaborate with Granica Research to translate advances in compression and probabilistic modeling into production-grade, industry-defining systems.
Latency as Intelligence. Drive system-wide initiatives to minimize latency from insight to decision, enabling faster model learning and data-driven reasoning.
Mastery of distributed systems: consensus, replication, consistency, and performance at scale.
Proven track record of architecting and delivering large-scale data or compute systems with measurable 10× impact.
Expertise with columnar formats and low-level data representation techniques.
Deep production experience with Spark, Flink, or next-generation compute frameworks.
Fluency in Java, Rust, Go, or C++, emphasizing simplicity, performance, and maintainability.
Demonstrated leadership—mentoring senior engineers, influencing architecture, and scaling technical excellence.
Systems intuition rooted in theory: compression, entropy, and information efficiency.
Familiarity with Iceberg, Delta Lake, or Hudi.
Published or open-source contributions in distributed systems, compression, or data representation.
Passion for bridging research and production to define the next frontier of efficient AI infrastructure.
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
At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring. Join us to build the foundational data systems that power the future of enterprise AI!
