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# Software Development Engineer - GPU Libraries

- **Company:** Advanced Micro Devices, Inc.
- **Location:** San Jose, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Generation, Nvidia CUDA, Computer Programming, Computer Engineering, Data Centers, Software Debugging, Python (Programming Language), Open Source Technology, OpenCL, Performance Tuning, Software Engineering, Graphics Processing Unit (GPU), Large Language Models, Gpu Programming, Information Technology, Hardware Acceleration, ROCm, Triton Inference Server, Programming Languages
- **Published:** October 31, 2026
- **Apply:** https://www.sanjosejobsite.com/job.asp?id=3481639808&tx=DT11202UYD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

engineer who is passionate about advancing the frontiers of AI. You thrive in open-source environments, enjoy tackling complex technical challenges, and take pride in writing efficient, maintainable, and scalable software. You are collaborative, curious, and excited to contribute to the

open-source

repositories, * Core Programming: Proficiency in C++, Python, or similar programming languages.

 * Domain Expertise: Strong foundational knowledge of AI training, inference methodologies, and full-stack AI tool development/lifecycle integration.
 * Hardware Acceleration: Familiarity with GPU programming (CUDA, HIP, or OpenCL) and performance optimization techniques.

ACADEMIC CREDENTIALS:

 * Bachelor's or
Master's
degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent

## Description

* Open-Source Innovation: Contribute to ROCm, Verl, Ray, Triton Inference Server, FlashInfer, physical simulation, and other emerging open-source projects driving AI innovation.
 * Ecosystem Collaboration: Collaborate with leading partners and open-source communities to enable AI workloads and improve performance on data center GPUs.
 * AI Tool Engineering & Utilization: Architect, build, and deploy custom internal AI utilities, automated agents, and developer workflows while leveraging advanced LLMs to accelerate code generation, optimize custom kernels, and automate the debugging of GPU performance bottlenecks., AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.