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

About the job Remote | CUDA & GPU Kernel Optimization Engineer - $70-$90/hourWe 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 ResponsibilitiesProfessionals in this role may contribute to:GPU Kernel OptimizationAnalyze and optimize GPU kernels for performance, efficiency, and hardware utilizationReview kernel implementations and identify bottlenecks in memory access, occupancy, throughput, or execution patternsImprove performance outcomes using CUDA, HIP, shader programming, or related GPU programming modelsOptimize kernels even when limited background context is available for the underlying algorithmProfiler-Guided Performance AnalysisUse profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, memory behavior, and related performance signalsEvaluate when specific profiler metrics are useful, misleading, or secondary to other optimization factorsDocument optimization decisions clearly and explain tradeoffs in technical termsCalibrate performance judgments against structured benchmarks, hardware constraints, and project-specific criteria C++, Python & GPU Programming ReviewWrite, modify, and reason about C++17, Python, and GPU programming codeReview code for correctness, performance impact, maintainability, and optimization potentialUse Git-based workflows to manage technical materials and project submissionsApply 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 optimizationFluency in core C++ features through C++17Working knowledge of Python and GitFluency in at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programmingAt least 1 year of professional or graduate-level research experience working with GPUsStrong understanding of GPU profiler performance metrics and how to use them to optimize kernelsAbility to work independently on technical review and optimization tasksAvailability to work at least 20 hours per week depending on project scope Educational BackgroundA degree in computer science, electrical engineering, computer engineering, applied mathematics, physics, mechanical engineering, or a related technical field is helpfulGraduate-level research, professional GPU engineering experience, or equivalent hands-on kernel optimization experience is highly relevantPractical experience with CUDA, HIP, GPU architecture, high-performance computing, graphics programming, or compiler-adjacent performance work may be especially valuable Nice to HaveExperience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimizationExperience optimizing kernels for NVIDIA Blackwell hardware or other modern GPU architecturesFamiliarity with Nsight Compute or comparable GPU profiling toolsPrior experience with GPU hardware organizations such as NVIDIA, AMD, Qualcomm, or similar technical environmentsOpen-source contributions related to GPU kernel optimization, HPC, compiler tooling, graphics, or performance engineering Why This OpportunityApply advanced GPU programming expertise to structured remote project workContribute to high-quality kernel optimization, performance review, and technical evaluation workflowsWork on flexible assignments aligned with CUDA, C++, profiler analysis, and GPU architecture strengthsUse your ability to identify bottlenecks, improve performance, and explain optimization decisions clearlyRemote structure with competitive hourly compensation Contract DetailsIndependent contractor roleFully remote with flexible schedulingEligible professionals may be based in approved project locations depending on project needsExpected commitment of at least 20 hours per week depending on project availabilityCompetitive rates between $70-$90 per hour depending on expertise and project scopeWeekly payments via Stripe or WiseProjects may be extended, shortened, or adjusted depending on scope and performanceWork 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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