Remote GPU Software Engineer (CUDA) for AI & HPC

Bright Vision Technologies

Farmington Hills (MI)

On-site

USD 80,000 - 107,000

Full time

9 days ago
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Job summary

Bright Vision Technologies is seeking a GPU Software Engineer (CUDA) to design and optimize compute-intensive workloads on modern accelerator hardware. You will work on AI training, inference, scientific computing, and high-throughput data processing, delivering measurable performance improvements in production.

The role requires deep CUDA expertise, production profiling, and collaboration with cross-functional teams.

Qualifications

  • 7+ years of experience in GPU programming and performance engineering.
  • Deep expertise in CUDA C/C++ and GPU programming models.
  • Knowledge of modern GPU architectures, memory hierarchies and execution models.
  • Hands-on experience profiling and optimizing GPU workloads in production.
  • Strong C++ skills and modern systems programming practices.
  • Experience integrating custom kernels into ML frameworks.
  • Strong communication and collaboration with research and engineering teams.

Responsibilities

  • Design and optimize compute-intensive workloads on modern GPU platforms.
  • Collaborate with product, design, engineering, and operations to translate requirements into engineered solutions.
  • Mentor junior engineers and contribute to code and design reviews.
  • Improve performance and scalability for AI training and inference workloads.
  • Contribute to production-grade GPU software and tooling.

Skills

CUDA C/C++
GPU programming
Performance engineering
C++ skills
Linear algebra
Communication skills

Education

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

Tools

NCCL
MPI
ML frameworks (integration)

Job description

Bright Vision Technologies is seeking a GPU Software Engineer (CUDA) to design and optimize compute-intensive workloads on modern accelerator hardware. You will work on AI training, inference, scientific computing, and high-throughput data processing, delivering measurable performance improvements in production.

The role requires deep CUDA expertise, production profiling, and collaboration with cross-functional teams.

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