ML Research Engineer - Hardware Codesign
About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated wi
What this role actually needs.
About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated wi 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. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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.
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ML Research Engineer - Hardware Codesign is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.
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Source: jobs.ashbyhq.com · Source ID: 5931abef-191b-417e-89f1-1d06f00e908c · Confidence: 97/100 · Last checked: May 7, 2026
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