Remote CUDA Engineering Expert - AI Trainer ($300-$300 per hour)

## **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