CUDA Engineer - Kernel Optimization

1. Role Overview Mercor is seeking GPU kernel optimization experts to contribute to a project with a leading AI lab. This opportunity is designed for freelancers with strong C++ skills, practical GPU programming experience, and the ability to improve kernel performance using profiler-guided analysis. You'll help evaluate, optimize, and reason about GPU kernels across modern hardware environments. This is a contract-based opportunity for specialists who enjoy squeezing performance out of modern GPU architectures. 2. Key Responsibilities Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, and related signals to guide kernel improvements Review GPU kernel implementations and identify bottlenecks without requiring extensive background in the underlying algorithms Write, modify, and reason about C++17, Python, and GPU programming code Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes Document optimization decisions clearly, including when specific profiler metrics are or are not useful 3. Ideal Qualifications Available to work at least 20 hrs/wk Fluent in core C++ features through C++17 Working knowledge of Python and Git Fluent 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 optimize GPU kernels without needing deep prior context on every algorithm Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus Experience optimizing kernels for NVIDIA Blackwell hardware is a plus Familiarity with NSight Compute is a plus Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus Open-source contributions related to GPU kernel optimization are a plus #J-18808-Ljbffr Obsidian