Research-Hardware Codesign Engineer

Requirements

• Strong Python, and C++ or Rust, with a cautious attitude toward correctness and an intuition for clean extensibility.
• Experience writing Triton, CUDA, or similar, and an understanding of the resulting mapping of tensor ops to functional units.
• Working knowledge of PyTorch or JAX; experience in large ML codebases is a plus.
• Practical understanding of floating point numerics, the ML tradeoffs of reduced precision, and the current state of the art in model quantization.
• Deep understanding of transformer models, and strong intuition for transformer rooflines and the tradeoffs of sharded training and inference in large-scale ML systems.
• Experience writing RTL (especially for floating point logic) and understanding of PPA tradeoffs is a plus.
• Strong cross-functional communication (e.g. across ML researchers and hardware engineers); ability to slice ambiguous early-incubation ideas into concrete arenas in which progress can be made.

Benefits

• Relocation assistance available