jazzhr
Parallel Computing Engineer
Bright Vision Technologies
Remote, USA
Full-time
Senior · 10+ years experience
Remote
USD 130000-180000 yearly / year
Discovered Yesterday
CUDANVIDIA Nsight SystemsCUDA ProfilerNCCLMPIPyTorch
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Role Overview
Bright Vision Technologies is seeking a highly experienced Parallel Computing Engineer with 10+ years of experience in High-Performance Computing (HPC), GPU programming, and parallel computing to optimize AI, machine learning, and scientific computing workloads. The ideal candidate will possess deep expertise in CUDA, GPU architecture, distributed computing, performance optimization, and large-scale AI infrastructure , with a proven track record of designing high-performance computing solutions for enterprise and research environments.
Key Skills for This Role
CUDANVIDIA Nsight SystemsCUDA ProfilerNCCLMPIPyTorch
Full Job Posting
Location
- 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $130,000–$180,000 Annually Experience Required: 10+ Years
- Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary
Bright Vision Technologies is seeking a highly experienced Parallel Computing Engineer with 10+ years of experience in High-Performance Computing (HPC), GPU programming, and parallel computing to optimize AI, machine learning, and scientific computing workloads. The ideal candidate will possess deep expertise in CUDA, GPU architecture, distributed computing, performance optimization, and large-scale AI infrastructure , with a proven track record of designing high-performance computing solutions for enterprise and research environments.
Key Responsibilities
- Design, develop, and optimize high-performance CUDA kernels for AI, deep learning, and scientific computing applications.
- Analyze, profile, and optimize GPU workloads using NVIDIA Nsight Systems, Nsight Compute, CUDA Profiler, and related performance analysis tools .
- Optimize GPU memory management, kernel execution, multi-GPU scaling, and distributed computing performance.
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