Applied AI Research Engineer – ML Systems & Structured Data

Job Overview

Granica is building the next generation of efficient AI infrastructure. We focus on eliminating inefficiency in AI systems by optimizing the data that feeds them. Our mission is to design data infrastructure that continuously improves how information is represented and used by AI. This role focuses on building machine learning systems for structured and tabular data rather than general LLM application development.

Key Responsibilities

• Turn research into working systems by transforming foundational ideas into scalable algorithms and prototypes
• Build evaluation harnesses, datasets, and benchmarks to 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
• Build hybrid AI systems that integrate symbolic, relational, and neural components
• Collaborate across research and engineering teams to validate hypotheses and integrate algorithms
• Run controlled experiments and analyze performance improvements
• Drive the cycle from prototype to production to optimization

Required Qualifications

• Strong background in machine learning, probabilistic modeling, optimization, or large-scale ML systems
• Experience building algorithms for structured, relational, tabular, or graph data
• Hands-on experience with PyTorch, JAX, TensorFlow, or similar ML frameworks
• Strong programming skills in Python
• Experience with systems languages such as Rust, C++, or CUDA
• Experience building large-scale ML pipelines, evaluation frameworks, or distributed systems
• Proven ability to turn research ideas into performant, reliable code
• Comfort working in research-driven environments with ambiguous problem definitions
• Strong experimentation discipline and focus on measurable performance improvements

About Granica

Granica is building the systems that close the gap between AI systems and structured data. Our research effort is led by Prof. Andrea Montanari (Stanford) and focuses on building learning systems that operate efficiently on large-scale structured and tabular data. We are shaping the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.

Benefits

Competitive salary, meaningful equity, and substantial bonus for top performers. Flexible time off plus comprehensive health coverage for you and your family. Support for research, publication, and deep technical exploration.