CUDA Engineering Expert

aitrainer; Deutschland; Vor Ort; EUR 50.000 - 80.000; Teilzeit

Zusammenfassung

aitrainer is looking for GPU kernel optimization experts to contribute to a project with a leading AI lab. The role is designed for freelancers with strong C++ skills and GPU programming experience, focusing on optimizing kernel performance using profiler-guided analysis.

You will analyze and optimize GPU kernels, use profiler metrics to guide improvements, and document decisions clearly. This opportunity allows for flexible working hours (minimum 20 hrs/wk) with a focus on performance optimization across GPU architectures.

Qualifikationen

• Available to work at least 20 hrs/week.
• Fluent in core C++ features through C++17.
• Strong understanding of GPU profiler performance metrics.

Aufgaben

• Analyze and optimize GPU kernels for performance.
• Use profiler metrics to guide kernel improvements.
• Document optimization decisions clearly.

Kenntnisse

C++17 proficiency

GPU programming experience

Profiler-guided optimization

Python knowledge

Git proficiency

Ausbildung

Professional or graduate-level experience with GPUs

Tools

CUDA

HIP

NSight Compute

Jobbeschreibung

Role Overview

Mercor is 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.