Remote CUDA Engineer for High-Performance AI & HPC

Bright Vision Technologies; San Diego, 4S Ranch (CA, CA); Remote; USD 85,000 - 110,000; Full time

Job summary

Bright Vision Technologies is seeking a CUDA Developer to architect and optimize compute-intensive workloads on modern GPUs for AI training, inference, and scientific computing. You will push performance, analyze bottlenecks, and deliver production-grade kernels and integration with ML frameworks.

The role requires 6+ years of GPU programming, strong C++ skills, and collaboration with product, engineering, and operations teams.

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.
• Strong understanding of modern GPU architectures, memory hierarchies, and execution models.
• Hands-on experience profiling and optimizing GPU workloads in production.
• Familiarity with NCCL, MPI, and high-performance interconnect technologies.
• Experience integrating custom kernels into ML frameworks.
• Strong C++ skills and familiarity with modern systems programming practices.
• Solid grounding in linear algebra and numerical methods.
• Strong communication and collaboration skills with research and engineering teams.

Responsibilities

• Design and optimize compute-intensive workloads on modern GPUs for AI training and inference.
• Translate ambiguous requirements into well-engineered GPU kernels and optimized pipelines.
• Collaborate with product, design, engineering, operations, and business stakeholders.
• Mentor junior engineers and contribute to code and design reviews.

Skills

CUDA C/C++

GPU performance tuning

Modern C++

Numerical methods

Communication & teamwork

Education

Bachelor's or Master's in CS/CE

Tools

NCCL

MPI

CUDA toolkit

Job description

Bright Vision Technologies is seeking a CUDA Developer to architect and optimize compute-intensive workloads on modern GPUs for AI training, inference, and scientific computing. You will push performance, analyze bottlenecks, and deliver production-grade kernels and integration with ML frameworks.

The role requires 6+ years of GPU programming, strong C++ skills, and collaboration with product, engineering, and operations teams.