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Salary
$120k – $200k per year
Location
In office (Atlanta)

Confirmed on the employer's own hiring board on Sep 27, 2026. First seen by Alion on Sep 27, 2026.

Overview
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YO AI Labs is an artificial intelligence platform specializing in AI model training, evaluation, and data annotation services. Headquartered in San Francisco, California, United States, the company connects domain experts and remote specialists across the globe to review, test, and benchmark generative AI model outputs. Through its decentralized workforce solutions and structured evaluation workflows, it enables frontier technology developers to enhance the accuracy, safety, and reliability of next-generation artificial intelligence systems.

CUDA Engineering Expert

Job Type: Contractor

Location: Remote

Job Overview

We are seeking experienced CUDA Engineering Experts to support a cutting-edge GPU optimization project. You will apply your expertise in CUDA, C++, and GPU programming to analyze, optimize, and improve high-performance GPU kernels.

No prior AI experience is required.

Key Responsibilities

  • Analyze, profile, and optimize GPU kernels using CUDA and profiling tools.

  • Identify performance bottlenecks and develop targeted optimization strategies.

  • Refactor C++ and CUDA code for efficiency and maintainability.

  • Develop shader logic using GLSL and WebGPU.

  • Document optimization processes, findings, and performance improvements.

  • Contribute to GPU architecture and performance discussions.

  • Collaborate with remote, cross-functional teams.

Required Qualifications

  • Strong expertise in CUDA programming and GPU kernel optimization.

  • Advanced C++ development skills.

  • Hands-on experience with GLSL and WebGPU.

  • Experience using GPU profiling tools such as NVIDIA Nsight or similar.

  • Strong understanding of GPU performance and architecture.

  • Excellent analytical, problem-solving, and technical communication skills.

  • Ability to work effectively in a remote environment.

Preferred Qualifications

  • Experience with high-performance computing or GPU-accelerated applications.

  • Experience optimizing workloads across different GPU architectures.

  • Background in graphics, compute shaders, or AI/ML acceleration.

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