GPU Kernel Optimization Specialist

weekday ai (yc w21) • United State
Remote
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AI Summary

We are seeking GPU kernel optimization experts to contribute to a project with a leading AI lab. You'll analyze and optimize GPU kernels for performance, efficiency, and hardware utilization using profiler-guided analysis. Requirements include fluency in C++17, Python, and at least one GPU programming model such as CUDA or HIP.

Key Highlights
Contract-based opportunity for GPU kernel optimization experts
Compensation: $80-$100 per hour
Requires C++17, Python, and CUDA/HIP expertise
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
Technical Skills Required
C++17 CUDA HIP
Benefits & Perks
Weekly payments on Stripe or Wise
Fully remote work
Nice to Have
CUDA C++ Core Libraries
inline PTX assembly
tensor core-level optimization
NSight Compute familiarity
Prior experience with NVIDIA, AMD, or Qualcomm
Open-source contributions related to GPU kernel optimization

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.

Requirements

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

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

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

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