Applied AI Research Engineer – ML Systems & Structured Data

Granica is building the next generation of efficient AI infrastructure, focusing on eliminating inefficiencies in AI systems that operate on structured enterprise data. The Applied AI Research Engineer will transform research ideas into scalable algorithms and production-ready ML systems, collaborating across research and engineering to optimize performance and improve learning methods for structured data.

Responsibilities:

• Transform foundational ideas from Granica Research and Prof. Andrea Montanari’s group into scalable algorithms and prototypes
• Build evaluation harnesses, datasets, and benchmarks that measure real signal from research ideas
• Define and improve metrics that quantify progress in structured AI systems
• Develop efficient learning methods for relational, tabular, graph, and enterprise datasets
• Prototype representation learning architectures and compression-aware models
• Explore new approaches for learning from heterogeneous structured data
• Implement fast training and inference pipelines using PyTorch, JAX, or custom kernels
• Optimize memory usage, compute utilization, and data movement
• Improve cost, latency, and throughput for large-scale ML workloads
• Design systems integrating symbolic, relational, and neural components
• Enable AI models to reason over structured datasets without relying on text intermediaries
• Work with Research Scientists to validate hypotheses at scale
• Work with Systems Engineers to integrate algorithms into Granica’s data platform
• Work with Product Engineering to ship features powering real enterprise workloads
• Run controlled experiments and analyze performance improvements
• Deliver results with clear benchmarks and reproducible evaluations
• Drive the cycle from prototype → production → optimization