Verified Mercor opportunity
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 — 4. Application Process • Submit your resume or relevant technical background to get started • Qualified applicants may be asked to complete a brief technical assessment or submit additional information