Remote CUDA GPU Engineer – High-Performance AI/HPC

United States Digital Space LLC; United States; Remote; USD 100,000 - 175,000; Full time

Job summary

Bright Vision Technologies is seeking a GPU Software Engineer with deep expertise in CUDA programming, GPU architecture, and high-performance computing to design and optimize compute-intensive workloads on modern accelerators. This role focuses on extracting maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads.

The ideal candidate delivers measurable performance improvements in production GPU systems and partners

Qualifications

• 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.
• Hands-on experience profiling and optimizing GPU workloads in production.

Responsibilities

• Design and implement high-performance CUDA kernels for compute-intensive workloads.
• Profile and optimize GPU code using Nsight systems, Nsight compute, and CUDA profilers.
• Tune memory access patterns, occupancy, register usage, and shared memory.
• Develop optimized libraries for linear algebra, attention, and 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.

Skills

CUDA programming

GPU performance optimization

C++

Communication skills

Education

Bachelor’s or Master’s degree in Computer Science or Computer Engineering

Tools

Nsight Systems

Nsight Compute

NCCL

MPI

Triton

CUTLASS

TensorRT

Job description

Bright Vision Technologies is seeking a GPU Software Engineer with deep expertise in CUDA programming, GPU architecture, and high-performance computing to design and optimize compute-intensive workloads on modern accelerators. This role focuses on extracting maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads.

The ideal candidate delivers measurable performance improvements in production GPU systems and partners