Remote GPU Systems Engineer: CUDA & HPC Expert

Bright Vision Technologies; Renton (WA); On-site; USD 90,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a GPU Systems Engineer for a fully remote role across the U.S. The position focuses on designing and optimizing compute-intensive workloads on modern GPU accelerators for AI training, inference, and HPC workloads.

The ideal candidate has deep CUDA expertise, solid C++ skills, and a track record of shipping high-performance GPU systems in production, with opportunities to mentor others and influence architecture choices.

Qualifications

• Six or more years of 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.
• Experience integrating custom kernels into ML frameworks.

Responsibilities

• Design and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use cases.
• Profile and optimize GPU code using 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 and high-performance networking.
• Collaborate with ML engineers to identify performance bottlenecks in training and inference pipelines.

Skills

CUDA programming

GPU architecture

High-performance computing

C++ proficiency

Profiling GPU workloads

ML frameworks integration

NCCL & MPI

PyTorch/JAX/Triton integration

Memory hierarchy tuning

Education

Bachelor’s or Master’s degree in CS/CE or related field

Tools

Nsight Systems

Nsight Compute

CUDA profilers

NCCL

MPI

RDMA

Triton

TensorRT

LLVM/MLIR

Job description

Bright Vision Technologies is seeking a GPU Systems Engineer for a fully remote role across the U.S. The position focuses on designing and optimizing compute-intensive workloads on modern GPU accelerators for AI training, inference, and HPC workloads.

The ideal candidate has deep CUDA expertise, solid C++ skills, and a track record of shipping high-performance GPU systems in production, with opportunities to mentor others and influence architecture choices.