CUDA Engineering Expert

Weekday AI; United States; Remote; USD 110,000 - 138,000; Part time

Job summary

Weekday AI is seeking a GPU kernel optimization expert for a freelance project with a leading AI lab. You will analyze and optimize GPU kernels, using CUDA/HIP and profilers to squeeze performance across modern hardware.

This contract-based role requires strong C++17, Python, and GPU programming skills and the ability to work remotely on your own schedule. Ideal candidates will have at least 1 year of GPU experience, comfort with Nsight Compute, and a track record of performance improvements.

Qualifications

• Proficient in modern C++ (C++17)
• Experience with GPU kernels and performance profiling
• Familiarity with CUDA/HIP or other GPU programming models
• Knowledge of GPU hardware architecture and perf metrics

Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization
• Use profiler metrics to guide kernel improvements
• Review GPU kernel implementations and identify bottlenecks
• 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

Skills

C++17

Python

GPU programming

CUDA

HIP

Shader programming

Profiling / performance analysis

Tools

Git

NSight Compute

CUDA C++ Core Libraries

Job description

This role is for one of our clients

Compensation: $80-$100 per hour

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.

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

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

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

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.