CUDA Engineering Expert

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

Requirements

• 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
• Able 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

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
• Able 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

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.

Contract and Payment Terms

• You will be engaged as an independent contractor
• This is a fully remote role that can be completed on your own schedule
• Projects can be extended, shortened, or concluded early depending on needs and performance
• Your work will not involve access to confidential or proprietary information from any employer, client, or institution
• Payments are weekly on Stripe or Wise based on services rendered

Please note: We are unable to support H1-B or STEM OPT candidates at this time