GPU Systems Engineer, CUDA

Location

California

Posted

10 days ago

Salary

0

Seniority

Senior

Bachelor Degree6 yrs expEnglishNode.jsPyTorch

Job Description

GPU Systems Engineer, CUDA

Bright Vision Technologies

• Design and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use cases. • Profile and optimize GPU code using tools such as Nsight Systems, Nsight Compute, and CUDA profilers. • Tune memory access patterns, occupancy, register usage, and shared memory utilization for peak performance. • Develop highly optimized libraries for linear algebra, attention, and other ML primitives. • Optimize multi-GPU and multi-node training using NCCL, RDMA, and high-performance networking. • Implement custom operators and fused kernels in PyTorch, JAX, or Triton. • Collaborate with ML engineers to identify performance bottlenecks in training and inference pipelines. • Develop benchmarks and regression tests to safeguard performance over time. • Evaluate new GPU architectures and feature sets, and advise on adoption strategy. • Contribute to compiler-level optimizations for tensor programs where appropriate, working at the boundary between ML frameworks and underlying accelerator codegen to unlock performance not reachable through framework-level tuning alone. • Optimize memory hierarchy usage across HBM, L2, shared memory, and registers. • Implement mixed-precision and quantized compute paths that maximize accelerator throughput while preserving numerical fidelity within bounds acceptable for the target workloads. • Document performance characteristics, design decisions, and tuning playbooks for internal teams. • Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies.

Job Requirements

  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
  • Six or more years of experience in GPU programming and performance engineering.
  • Deep expertise in CUDA C/C++ and GPU programming models.
  • Strong understanding of modern GPU architectures, memory hierarchies, and execution models.
  • Hands-on experience profiling and optimizing GPU workloads in production.
  • Familiarity with NCCL, MPI, and high-performance interconnect technologies.
  • Experience integrating custom kernels into ML frameworks.
  • Strong C++ skills and familiarity with modern systems programming practices.
  • Solid grounding in linear algebra and numerical methods.
  • Strong communication and collaboration skills with research and engineering teams.

Benefits

  • Competitive base salary commensurate with experience, plus benefits

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