CUDA Engineering Expert

Weekday AI

12h•$80 - $100•Remote

About The Position

We are 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.

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
Ability to optimize GPU kernels without needing deep prior context on every algorithm

Nice To Haves

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

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