Remote CUDA GPU Software Engineer for HPC/ML

Bright Vision Technologies; Oregon; Híbrido; USD 80.000 - 107.000; Jornada completa

Descripción de la vacante

Bright Vision Technologies is seeking a GPU Software Engineer (CUDA) for 100% remote work in the United States. The role emphasizes CUDA programming, GP U architecture, and high-performance computing to optimize workloads for AI training, inference, and data processing on modern GPUs.

The successful candidate will work with cross-functional teams, ship production-grade kernels, and mentor junior engineers while contributing to performance improvements and scalable GPU solutions.

Formación

• 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.

Conocimientos

CUDA C/C++

GPU programming

Performance engineering

C++

Profiling

NCCL/MPI

ML frameworks integration

GPU architectures

Linear algebra

Communication

Educación

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

Herramientas

Triton

CUTLASS

TensorRT

FasterTransformer

vLLM internals

Descripción del empleo

Bright Vision Technologies is seeking a GPU Software Engineer (CUDA) for 100% remote work in the United States. The role emphasizes CUDA programming, GP U architecture, and high-performance computing to optimize workloads for AI training, inference, and data processing on modern GPUs.

The successful candidate will work with cross-functional teams, ship production-grade kernels, and mentor junior engineers while contributing to performance improvements and scalable GPU solutions.