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You will analyze, profile, and optimize GPU kernels to maximize computational throughput while collaborating with stakeholders to identify and resolve bottlenecks. Additionally, you will refactor C++ and CUDA codebases and implement shader logic using GLSL and WebGPU to ensure seamless pipeline integration.
Role Title: CUDA Engineering Expert
Role Type: Contractor
Location: Remote
We are engaging CUDA Engineering Experts to contribute to a cutting-edge customer project focused on GPU kernel optimization in collaboration with a leading AI lab. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Required Skills
CUDA
C++
GLSL
WebGPU
Scope of Work
Analyze, profile, and optimize GPU kernels using CUDA and relevant profiling tools to maximize computational throughput on modern hardware.
Collaborate with project stakeholders to assess and identify kernel bottlenecks, proposing targeted optimization strategies.
Refactor C++ and CUDA codebases for improved maintainability, efficiency, and adaptability across diverse GPU architectures.
Implement shader logic and graphics workflows using GLSL and WebGPU, ensuring seamless integration with existing pipelines.
Document key findings, optimization steps, and performance improvements with clear, actionable reports and technical communication.
Contribute expertise to design discussions, supporting the evaluation of new GPU-based approaches and performance metrics.
Stay informed on advancements in GPU programming and share relevant insights to enhance project outcomes.
Preferred Qualifications
Demonstrated expertise in CUDA programming, with a strong track record of performance-tuning GPU kernels.
Advanced C++ development skills, particularly in high-performance computing environments.
Hands-on experience with GLSL and WebGPU for graphics and compute shader development.
Proficiency using GPU profilers (such as Nsight, Visual Profiler, or similar tools) for guided optimization.
Strong analytical abilities to evaluate and reason about kernel performance across hardware generations.
Excellent written and verbal communication skills—clear documentation and technical reporting are essential.
Experience collaborating in remote, cross-disciplinary project settings is a plus.
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