San Francisco, California, United States · On-site
$190k–$250k/yr
The Mission 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 …
Skills: Distributed Systems, Java, Rust, Go, C++
San Francisco, California, United States · Hybrid
Location: Mountain View, CA (On-site) Overview Diffusion models have transformed image, video, and multimodal AI. We're applying those ideas to one of the next frontiers in machine learning. At Granica, we're building La…
Skills: Diffusion Models, Generative Learning Algorithms, Representation Learning, PyTorch, JAX
San Francisco, California, United States • On-site
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Architect and build self-optimizing data infrastructure and metadata substrates to support petabyte-scale AI datasets. Implement research-driven algorithms for compression and representation to optimize data layout and compute pipelines.
Requires deep expertise in distributed systems, columnar formats, and proficiency in languages like Java, Rust, Go, or C++. Candidates should have production experience with Spark or Flink and an interest in the mathematics of compression and entropy.
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 performance bonus for top performers
401(k) with company match, comprehensive health coverage, and unlimited PTO
Daily catered meals in our Mountain View office
Support for research, publication, and conference participation
At Granica, you'll help build the next generation of enterprise AI—from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.
AI infrastructure that lets enterprises own their data and the intelligence built on it, and scale both efficiently.
Granica is an AI research and products company building a new kind of full-stack AI infrastructure for the 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, from raw data to frontier models to the agents that act on them.
Offices: 287 Castro Street, Mountain View, California 94041, US
AI infrastructure that lets enterprises own their data and the intelligence built on it, and scale both efficiently.
Granica is an AI research and products company building a new kind of full-stack AI infrastructure for the 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, from raw data to frontier models to the agents that act on them.
Offices: 287 Castro Street, Mountain View, California 94041, US
