Remote GPU Systems Engineer - HPC/AI Kernel Dev

Bright Vision Technologies; Highlands Ranch (CO); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a GPU Systems Engineer to design and optimize CUDA kernels for AI and HPC workloads, focusing on extracting peak performance from modern accelerator hardware. You will work with cross-functional teams to translate requirements into efficient, production-grade solutions and mentor junior engineers.

The role emphasizes deep CUDA programming, GPU architecture mastery, and performance tuning across CPU-GPU boundaries, with remote collaboration and a strong focus

Qualifications

• Bachelor’s or Master’s degree in CS/CE or related field.
• 6+ years of GPU programming and performance engineering.
• Expertise in CUDA C/C++ and GPU architectures.
• Experience profiling and optimizing GPU workloads in production.

Responsibilities

• Design and implement high-performance CUDA kernels for AI and HPC workloads.
• Profile and optimize GPU code using Nsight tools and CUDA profilers.
• Tune memory access patterns, occupancy, and shared memory for peak performance.
• Develop optimized libraries for ML primitives and linear algebra.
• Optimize multi-GPU and multi-node training using NCCL and high-performance networking.
• Collaborate with ML engineers to identify bottlenecks in training and inference.

Skills

CUDA programming

GPU performance

C++ programming

Profiling tools

NCCL/MPI

Education

Bachelor's degree in CS/CE

Master's degree in CS/CE

Tools

Nsight Systems

Nsight Compute

CUDA profilers

Triton

CUTLASS

TensorRT

Job description

Bright Vision Technologies is seeking a GPU Systems Engineer to design and optimize CUDA kernels for AI and HPC workloads, focusing on extracting peak performance from modern accelerator hardware. You will work with cross-functional teams to translate requirements into efficient, production-grade solutions and mentor junior engineers.

The role emphasizes deep CUDA programming, GPU architecture mastery, and performance tuning across CPU-GPU boundaries, with remote collaboration and a strong focus