Improve the performance, efficiency, and hardware utilization of GPU kernels across modern hardware environments. This per-task contract opportunity focuses on profiler-guided optimization for specialists who enjoy extracting more performance from GPU architectures.
Key Responsibilities
- Analyze, evaluate, and optimize GPU kernels for performance, efficiency, and hardware utilization.
- Use metrics such as L2 cache hit rate, L2 throughput, occupancy, and related profiler signals to guide improvements.
- Review kernel implementations and identify bottlenecks without requiring extensive prior knowledge of 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.
- Clearly document optimization decisions, including when particular profiler metrics are or are not useful.
Qualifications
- Fluency with core C++ features through C++17.
- Working knowledge of Python and Git.
- Fluency in at least one GPU programming model, including CUDA, HIP, Slang, HLSL, GLSL, or a related kernel programming approach.
- At least 1 year of professional or graduate-level GPU research experience.
- Strong understanding of GPU profiler metrics and their use in kernel optimization.
- Ability to optimize GPU kernels without deep prior context for 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.
Work Terms
- Remote, per-task contract engagement.
- Availability of at least 20 hours per week is required.
Compensation
- Compensation is 500 per task.
Application Process
- Submit a resume or relevant technical background for consideration.
- Qualified applicants may be asked to complete a brief technical assessment or provide additional information.