Senior Solutions Architect, AI Performance Engineering

Responsibilities

• Engaging directly with key AV application engineers to understand the current and future problems they are solving. You will develop and improve fundamental parallel algorithms and data structures. You will provide efficient solutions using GPUs through library development and direct application contributions.
• Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the build of next-generation architectures, software platforms, and programming models.
• Engaging in deep optimization of high-performance operators, involving but not limited to GPU kernel optimization, instruction-level tuning, and compiler optimization. These optimizations will directly support customers to use NVIDIA libraries such as cuDNN, cuBLAS, and CUTLASS and Open- source libs like DeepGEMM,…
• Improving communication for Physical AI-related distributed transformer workloads by employing communication tools developed by NVIDIA. These include NCCL, NCCL GIN, and NVSHMEM, as well as open-source solutions like DeepEP and NCCL EP. This demands in-depth study of interconnect topologies (NVLINK) and network…
• What we need to see
• BSc/MSc/PhD or equivalent experience in Computer Science, Electrical Engineering, Physics, Mathematics, or a related technical field.
• 8+ years of hands-on validated ML/DL performance engineering experience with focus on improving GPU compute efficiency of training and inferencing workloads.
• Experience with C, C++, or Python and proficiency with Linux.
• Solid understanding of software development, programming techniques, and algorithms.
• Strong mathematical fundamentals, including linear algebra and numerical methods.

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