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Principal Software Engineer – PyTorch Training Frameworks

AMD · San Jose, California, United States

Information Technology Software & Development Full-time Posted 4 days ago

About this role

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture

We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career

PRINCIPAL SOFTWARE DEVELOPMENT ENGINEER – PYTORCH TRAINING FRAMEWORKS THE ROLE: AMD is looking for a Principal-level PyTorch training framework expert to help drive performance, scalability, and correctness of large-scale AI training on AMD Instinct™ accelerators. You will work at the intersection of PyTorch internals, distributed training, and hardware-aware optimization, partnering closely with compiler, kernel, driver, and architecture teams to deliver industry-leading training performance and developer experience. THE PERSON: The ideal candidate is deeply hands-on with PyTorch training and thrives on solving complex systems problems (performance, scaling, memory efficiency, distributed communication)

You bring strong technical leadership, can influence architecture across teams, and are comfortable driving ambiguity to crisp execution. You communicate clearly with both engineers and stakeholders and can represent AMD credibly in upstream/open-source discussions. KEY RESPONSIBILITIES: Act as a technical authority for PyTorch training at AMD, setting direction for performance, scalability, and reliability Drive optimization of key PyTorch training workloads (LLMs/foundation models) across single-node and multi-node systems Improve and debug trai

Apply for this role on AMD’s official careers site.

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