Granica is an AI research and infrastructure company focused on reliable, steerable representations for enterprise data. The role involves transforming research ideas into practical algorithms and production-ready systems, optimizing learning methods for structured AI, and collaborating across teams to integrate algorithms into the core data platform.
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
- Design hybrid learning systems that reason over structured data natively, not through text intermediaries
- Work with Research Scientists to validate hypotheses at scale
- Work with Systems Engineers to integrate your algorithms into Granica’s core data platform
- Work with Product Engineering to ship features that power live enterprise workloads
- Run controlled experiments, analyze performance deltas, and deliver results with clear benchmarks
- Drive the loop from prototype to production, improving the system each cycle