Remote GPU Systems Engineer: CUDA Performance & AI

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a GPU Systems Engineer to design high-performance CUDA kernels and optimize GPU workloads for AI and HPC use cases. The role focuses on extracting maximum throughput from modern accelerator hardware and requires deep CUDA knowledge and production experience.

You will work with cross-functional teams to translate requirements into optimized software, contribute to performance benchmarks, and mentor junior engineers while keeping up with new GPU architectures

Qualifications

• Bachelor’s or Master’s degree in CS/CE or related field.
• Six+ years of GPU programming and performance engineering experience.
• Strong expertise in CUDA C/C++ and GPU architectures.

Responsibilities

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

Skills

CUDA C/C++

GPU programming

Profiling

Multi-GPU training

Communication

Education

Bachelor’s or Master’s degree

Tools

Nsight Systems

Nsight Compute

CUDA profilers

NCCL

MPI

Job description

Bright Vision Technologies is seeking a GPU Systems Engineer to design high-performance CUDA kernels and optimize GPU workloads for AI and HPC use cases. The role focuses on extracting maximum throughput from modern accelerator hardware and requires deep CUDA knowledge and production experience.

You will work with cross-functional teams to translate requirements into optimized software, contribute to performance benchmarks, and mentor junior engineers while keeping up with new GPU architectures