Designs and implements custom networking collectives using C++ and CUDA for efficient AI model training on supercomputers. Requires expertise in low-level performance-critical software, RDMA distributed algorithms, and GPU/CPU code.
380k – 555k/yr
HybridBackend Engineering
About the role
Responsibilities
Collaborate closely with ML researchers to design and implement efficient collective operations in C++ and CUDA.
Ensure that our largest training jobs take full advantage of the different network transports used in our supercomputers.
Work on simulations to inform our future supercomputer network designs.
Requirements
Background in low level performance critical software.
Have written distributed algorithms using RDMA in the past.
Comfortable writing low level performance sensitive CPU and/or GPU code.
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