Remote | CUDA & GPU Kernel Optimization Engineer — $70–$90/hour

We are sharing a specialised part-time consulting opportunity for CUDA and GPU programming professionals experienced in kernel optimization, C++ engineering, profiler-guided performance analysis, GPU hardware utilization, and technical review. This role supports current and upcoming remote consulting opportunities focused on GPU kernel optimization, performance evaluation, CUDA/HIP review, profiler metric analysis, C++ and Python workflows, and high-quality project execution. Selected professionals will apply their GPU programming expertise to analyze kernels, identify performance bottlenecks, improve implementation quality, and document optimization decisions across modern hardware environments. Key Responsibilities Professionals in this role may contribute to: GPU Kernel Optimization Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization Review kernel implementations and identify bottlenecks in memory access, occupancy, throughput, or execution patterns Improve performance outcomes using CUDA, HIP, shader programming, or related GPU programming models Optimize kernels even when limited background context is available for the underlying algorithm Profiler-Guided Performance Analysis Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, memory behavior, and related performance signals Evaluate when specific profiler metrics are useful, misleading, or secondary to other optimization factors Document optimization decisions clearly and explain tradeoffs in technical terms Calibrate performance judgments against structured benchmarks, hardware constraints, and project-specific criteria C++, Python & GPU Programming Review Write, modify, and reason about C++17, Python, and GPU programming code Review code for correctness, performance impact, maintainability, and optimization potential Use Git-based workflows to manage technical materials and project submissions Apply practical GPU programming expertise across CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming environments Ideal Profile Strong candidates may have: Strong practical experience with GPU programming and kernel optimization Fluency in core C++ features through C++17 Working knowledge of Python and Git Fluency in at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming At least 1 year of professional or graduate-level research experience working with GPUs Strong understanding of GPU profiler performance metrics and how to use them to optimize kernels Ability to work independently on technical review and optimization tasks Availability to work at least 20 hours per week depending on project scope Educational Background A degree in computer science, electrical engineering, computer engineering, applied mathematics, physics, mechanical engineering, or a related technical field is helpful Graduate-level research, professional GPU engineering experience, or equivalent hands-on kernel optimization experience is highly relevant Practical experience with CUDA, HIP, GPU architecture, high-performance computing, graphics programming, or compiler-adjacent performance work may be especially valuable Nice to Have Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization Experience optimizing kernels for NVIDIA Blackwell hardware or other modern GPU architectures Familiarity with Nsight Compute or comparable GPU profiling tools Prior experience with GPU hardware organizations such as NVIDIA, AMD, Qualcomm, or similar technical environments Open-source contributions related to GPU kernel optimization, HPC, compiler tooling, graphics, or performance engineering Why This Opportunity Apply advanced GPU programming expertise to structured remote project work Contribute to high-quality kernel optimization, performance review, and technical evaluation workflows Work on flexible assignments aligned with CUDA, C++, profiler analysis, and GPU architecture strengths Use your ability to identify bottlenecks, improve performance, and explain optimization decisions clearly Remote structure with competitive hourly compensation Contract Details Independent contractor role Fully remote with flexible scheduling Eligible professionals may be based in approved project locations depending on project needs Expected commitment of at least 20 hours per week depending on project availability Competitive rates between $70–$90 per hour depending on expertise and project scope Weekly payments via Stripe or Wise Projects may be extended, shortened, or adjusted depending on scope and performance Work will not involve access to confidential or proprietary information from any employer, client, or institution About the Platform This opportunity is available through 24-MAG LLC. 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