GPU Programmer - Remote

YO AI Labs • Artificial Intelligence • LLM & Generative AI • JavaScript • C++ • 1 day ago
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Salary
$120k – $170k per year
Location
In office (Texas)

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

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YO AI Labs

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.
yoailabs.com • HQ: San Francisco, United States • Reinforcement Learning • LLM & Generative AI • Artificial Intelligence • 5000+ employees
Tech stack of YO AI Labs

GPU Programmer - Remote

Job Type: Contractor

Location: Remote

Job Overview

We are seeking experienced GPU Programmers / Software Engineers to design and optimize GPU-based tasks for AI and LLM applications. You will apply your expertise in GPU programming, performance optimization, and C++ development to build high-performance solutions.

Key Responsibilities

  • Design, implement, and optimize GPU software using CUDA, WebGPU, or GLSL.

  • Profile and optimize GPU kernels and shaders for performance and efficiency.

  • Develop host-side logic and GPU integrations using C++.

  • Create GPU-focused tasks and solutions for AI/LLM applications.

  • Analyze performance bottlenecks and implement optimization strategies.

Required Qualifications

  • Strong experience with GPU programming, particularly on NVIDIA GPUs.

  • Proficiency in CUDA, WebGPU, or GLSL.

  • Strong C++ programming skills.

  • Background in graphics programming, ML acceleration, scientific computing, HPC, or related GPU-focused fields.

  • Strong understanding of GPU architecture and performan

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