CUDA & GPU Kernel Optimization Engineer

Role Description

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

• 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.

Qualifications

• 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. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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