Granica is building the infrastructure for a new kind of intelligence: one that is structured, efficient, and deeply integrated with data. The business problem is that the current form of AI is not well-suited for structured data, which represents the world's most valuable data. Granica aims to close this gap by advancing the frontier of how data is represented, stored, and transformed to make large-scale intelligence creation sustainable and adaptive, leading to 'Efficient Intelligence'.
Requirements
- Strong background in machine learning, probabilistic modeling, optimization, or distributed learning.
- Experience building and tuning algorithms for structured, tabular, graph, or relational data.
- Hands-on experience with PyTorch, JAX, TensorFlow, or custom ML kernels.
- Strong Python skills and familiarity with systems languages such as Rust, C++, or CUDA.
- Experience with large-scale data pipelines, model evaluation frameworks, or distributed systems.
- Structured representation learning, tabular or multimodal models, or relational ML.
- Distributed data systems, query engines, or vector/embedding infrastructure.
Responsibilities
- Transform foundational ideas from Granica Research and Prof. Andrea Montanari’s group into scalable algorithms and experimental prototypes.
- Build the evaluation harnesses, metrics, and datasets that reveal real signal from research concepts.
- Define and refine the metrics that determine progress in structured AI.
- Develop efficient learning methods for relational, tabular, graph, and enterprise data.
- Prototype representation learning architectures and compression-aware models for large-scale structured information.
- Implement fast training and inference loops using PyTorch, JAX, or custom kernels.
- Optimize memory, compute, and data-movement paths with a focus on cost, latency, and throughput.
Other
- This is a high-ownership role for engineers who can think like researchers and build like systems experts.
- Comfort working in ambiguous research environments while delivering measurable outcomes.
- Curiosity for how structure and efficiency reshape the next generation of AI.
- Collaborate deeply across teams
- Work with Research Scientists to validate hypotheses at scale.
