Senior Software Engineer – Foundational Data Systems for AI

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.

• Global Metadata Substrate. Architect the transactional and metadata substrate that supports time-travel schema evolution and atomic consistency across petabyte-scale tabular datasets.

• Adaptive Engines. Build systems that reorganize data autonomously learning from access patterns and workloads to maintain peak efficiency without manual tuning.

• Intelligent Data Layouts. Optimize bit-level organization (encoding compression layout) to extract maximal signal per byte read.

• Autonomous Compute Pipelines. Develop distributed compute systems that scale predictively adapt to dynamic load and maintain reliability under failure.

• Research to Production. Implement new algorithms in compression representation and optimization emerging from ongoing research. Opportunities to publish and open-source are encouraged.

• Latency as Intelligence. Design for minimal time between question and insight enabling models and humans to learn faster from data.

• Depth in distributed systems: consensus partitioning replication 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 theory of the mathematics of compression entropy and learning efficiency.

• A builder’s mindset: pragmatic rigorous and grounded in long-term systems thinking.

• Familiarity with Iceberg Delta Lake or Hudi.

• Research or open-source contributions in compression indexing or distributed computation.

• Interest in how data representation affects training dynamics and model reasoning efficiency.

• 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!

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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