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You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity!</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Co-design future hardware for programmability and performance with our hardware vendors</li>\n</ul>\n<ul>\n<li>Assist hardware vendors in developing optimal kernels and add support for it in our compiler</li>\n</ul>\n<ul>\n<li>Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features</li>\n</ul>\n<ul>\n<li>Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking</li>\n</ul>\n<ul>\n<li>Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators</li>\n</ul>\n<ul>\n<li>Manage communication and coordination with internal and external partners</li>\n</ul>\n<ul>\n<li>Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads.</li>\n</ul>\n<ul>\n<li>Evaluate potential partners’ accelerators and platforms.</li>\n</ul>\n<ul>\n<li>As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings.</li>\n</ul>\n<p><strong>You might thrive in this role if you have:</strong></p>\n<ul>\n<li>4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware.</li>\n</ul>\n<ul>\n<li>Strong experience in software/hardware co-design</li>\n</ul>\n<ul>\n<li>Deep understanding of GPU and/or other AI accelerators</li>\n</ul>\n<ul>\n<li>Experience with CUDA, Triton or a related accelerator programming language</li>\n</ul>\n<ul>\n<li>Experience driving Machine Learning accuracy with low precision formats</li>\n</ul>\n<ul>\n<li>Experience with system performance modeling and analysis to optimize ML model deployment</li>\n</ul>\n<ul>\n<li>Strong coding skills in C/C++ and Python</li>\n</ul>\n<ul>\n<li>Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture.</li>\n</ul>\n<p><strong>These attributes are nice to have:</strong></p>\n<ul>\n<li>PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. 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