Software Engineer – Foundational Data Systems AI

Software Engineer – Foundational Data Systems for AI

2 months ago Be among the first 25 applicants

This range is provided by Agilesoft. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

Mid-Senior level

Full-time

Engineering and Information Technology

IT Services and IT Consulting

Granica is an AI research and systems company building the infrastructure for a new kind of intelligence: one that is structured, efficient, and deeply integrated with data.

Our systems operate at exabyte scale
, processing petabytes of data each day for some of the world’s most prominent enterprises in finance, technology, and industry. These systems are already making a measurable difference in how global organizations use data to deploy AI safely and efficiently.

We believe that the next generation of enterprise AI will not come from larger models but from more efficient data systems
. By advancing the frontier of how data is represented, stored, and transformed, we aim to make large-scale intelligence creation sustainable and adaptive.

Our long-term vision is Efficient Intelligence
: AI that learns using fewer resources, generalizes from less data, and reasons through structure rather than scale. To reach that, we are first building the Foundational Data Systems that make structured AI possible.

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. Help design and implement the metadata substrate that supports time-travel, schema evolution, and atomic consistency across massive tabular datasets.
• Adaptive Engines. Build components that reorganize data autonomously, learning from access patterns and workloads to maintain efficiency with minimal manual tuning.
• Intelligent Data Layouts. Develop and refine bit-level encodings, compression, and layout strategies to extract maximum signal per byte read.
• Autonomous Compute Pipelines. Contribute to distributed compute systems that scale predictably and adapt to dynamic load.
• Research to Production. Translate new algorithms in compression and representation from research into production-grade implementations.
• Latency as Intelligence. Design and optimize data paths to minimize time between question and insight, enabling faster learning for both models and humans.

• 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.
• A builder’s mindset—eager to learn, improve, and deliver features end-to-end with growing autonomy.

• Familiarity with Iceberg, Delta Lake, or Hudi.
• Contributions to open-source projects or research in compression, indexing, or distributed systems.
• Interest in how data representation influences AI training dynamics and reasoning efficiency.

• Fundamental Research Meets Enterprise Impact. Work…