Research Engineer – Machine Learning Systems

Responsibilities

• Build scalable training, evaluation, and inference pipelines for machine learning systems
• Implement and optimize algorithms for structured and tabular data
• Develop benchmarks, datasets, and evaluation frameworks for new research ideas
• Improve training efficiency, memory usage, and inference performance
• Prototype new ML systems and rapidly validate research ideas
• Collaborate closely with the research team to translate research into production systems

Requirements

• BS, MS, or PhD in Computer Science, Machine Learning, Mathematics, or a related field
• Strong software engineering and machine learning fundamentals
• Experience building production ML systems or ML infrastructure
• Hands-on experience with PyTorch or JAX
• Strong programming skills in Python
• Experience developing evaluation frameworks, ML pipelines, or distributed systems
• Ability to translate research ideas into reliable, production-quality software

Preferred Qualifications

• Experience with representation learning, structured or tabular data, or probabilistic modeling
• Experience with distributed training or ML systems optimization
• Experience working closely with research teams
• Experience optimizing training or inference at scale
• Experience with CUDA, C++, or Rust
• Contributions to open-source ML systems
• Publications or research experience in machine learning

Benefits

• Competitive salary, meaningful equity, and performance bonus
• 401(k) with company match
• Comprehensive health coverage
• Unlimited PTO
• Daily catered meals in the Mountain View office
• Support for research, publication, and conference participation

About the Company

Granica is building Large Tabular Models (LTMs)—foundation models that learn natively from structured and relational enterprise data. Our research explores how generative AI can learn more efficiently from enterprise data through better representations, data selection, augmentation, and compression.