Research Infrastructure Engineer, Training Systems

📋 Description

• Build and maintain infrastructure for large-scale model training and experimentation.
• Design APIs and interfaces that make complex training workflows easier to express and harder to misuse.
• Improve reliability, debuggability, and performance across training and data pipelines.
• Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage.
• Write tests, benchmarks, and diagnostics that catch meaningful regressions.

🎯 Requirements

• Strong systems instincts with focus on performance, reliability, and clean abstractions.
• Comfortable working across ML research code and production infrastructure.
• Good taste in API and interface design with empathy for researchers.
• Debug across Python, PyTorch, distributed systems, GPUs, networking.
• Write tests, benchmarks, and diagnostics to catch regressions.
• Proficient in Python and PyTorch for ML workflows.