Applied AI Research Engineer – ML Systems & Structured Data

Granica is building the next generation of efficient AI infrastructure. The Applied AI Research Engineer will focus on transforming research ideas into practical algorithms and production-ready ML systems that operate across large-scale structured and tabular data.

Responsibilities:

• Turn research into working systems
• 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
• Invent and optimize algorithms
• 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
• Build high-performance ML pipelines
• 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
• Build hybrid AI systems
• Design systems integrating symbolic, relational, and neural components
• Enable AI models to reason over structured datasets without relying on text intermediaries
• Collaborate across research and engineering
• 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
• Iterate fast and measure everything
• Run controlled experiments and analyze performance improvements
• Deliver results with clear benchmarks and reproducible evaluations
• Drive the cycle from prototype → production → optimization