Senior CUDA HPC Engineer — Remote

Talanto; Northern (KY); Hybrid; USD 130,000 - 180,000; Full time

Job summary

BV Teck is seeking a highly experienced Parallel Computing Engineer for a 100% remote role in the U.S. You will optimize AI and scientific workloads with CUDA, GPUs, and distributed architectures.

The ideal candidate has 10+ years in HPC, CUDA, and GPU programming, with proven leadership in GPU clusters and large-scale AI infrastructure. The role requires deep knowledge of CUDA, NCCL, MPI, and modern ML frameworks.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or related technical discipline.
• 10+ years of professional experience in GPU programming, HPC, or parallel computing.
• Expert-level proficiency in CUDA C/C++, GPU architecture, and massively parallel programming techniques.
• Experience with NCCL, MPI, CUDA-aware MPI, and distributed GPU communication frameworks.
• Strong understanding of GPU memory hierarchy, kernel optimization, and performance analysis.

Responsibilities

• Design, develop, and optimize CUDA kernels for AI, deep learning, and scientific computing.
• Profile and optimize GPU workloads using Nsight Systems/Compute, CUDA Profiler, and related tools.
• Optimize memory, kernel execution, multi-GPU scaling, and distributed computing performance.
• Design scalable distributed training and inference architectures using NCCL, MPI, and CUDA-aware libraries.
• Develop custom GPU kernels for PyTorch, TensorFlow, JAX, Triton, or similar frameworks.
• Improve training/inference performance for large-scale AI workloads.
• Mentor engineers and provide technical leadership in GPU optimization and HPC architecture.

Skills

CUDA C/C++

GPU programming

HPC

Parallel computing

C/C++ debugging

Education

Bachelor's/Master's in CS/CE/EE

Tools

NCCL

MPI

NVIDIA Nsight

CUDA Profiler

PyTorch/TensorFlow/JAX

Job description

BV Teck is seeking a highly experienced Parallel Computing Engineer for a 100% remote role in the U.S. You will optimize AI and scientific workloads with CUDA, GPUs, and distributed architectures.

The ideal candidate has 10+ years in HPC, CUDA, and GPU programming, with proven leadership in GPU clusters and large-scale AI infrastructure. The role requires deep knowledge of CUDA, NCCL, MPI, and modern ML frameworks.