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Senior Solutions Architect, AI Performance Engineering

NVIDIA· Architect, Solutions
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Santa Clara, CA$184k - $287.5kFull-timePosted 7mo ago

About the role

Solutions Architect with a performance engineering focus to accelerate Physical AI workloads for Autonomous Vehicles and Robotics using NVIDIA full-stack technologies. Works with Automotive Solutions Architecture to identify bottlenecks, optimize high-performance workloads, and influence next-generation hardware/software platforms.

Responsibilities

  • Engaging directly with key AV application engineers to understand current and future problems; develop and improve fundamental parallel algorithms and data structures; provide efficient GPU-based solutions through library development and direct application contributions.
  • Collaborating with architecture, research, libraries, tools, and system software teams to influence next-generation architectures, software platforms, and programming models.
  • Engaging in deep optimization of high-performance operators, including GPU kernel optimization, instruction-level tuning, and compiler optimization to support NVIDIA libraries (cuDNN, cuBLAS, CUTLASS) and open-source libs (DeepGEMM, FlashMLA, FlashAttention, Flashinfer, etc.).
  • Improving communication for Physical AI-related distributed transformer workloads by employing NVIDIA communication tools (NCCL, NCCL GIN, NVSHMEM) and open-source solutions (DeepEP, NCCL EP); studying interconnect topologies (NVLINK) and network protocols (InfiniBand/RoCE) to develop efficient data transfer strategies and compute-communication overlap.

Qualifications

  • BSc/MSc/PhD or equivalent experience in Computer Science, Electrical Engineering, Physics, Mathematics, or related technical field.
  • 8+ years of hands-on validated ML/DL performance engineering experience focused on improving GPU compute efficiency of training and inference 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.
  • Background in parallel programming and high-performance computing with extensive knowledge of parallel architectures and performance analysis/tuning; GPU programming experience desirable.
  • Experience in distributed communication optimization (RDMA, GPU interconnects, collective communication algorithms, open-source libraries for large-scale model training/inference).
  • Effective verbal/written communication and technical presentation skills (blog posts, GitHub, PPT).

Skills

Required:GPU programmingC/C++/PythonLinuxCommunication skills
Nice to have:CUDA kernelsNsight System / Nsight ComputeDistributed communication optimization

Benefits

  • Competitive salaries and comprehensive benefits package.
  • Equity eligibility.
  • See www.nvidiabenefits.com for details.
Last updated Jul 24, 2026. Interested? Apply on the company site.

About NVIDIA

World Leader in AI Computing

NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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