Principal ML Engineer - Large Scale Training Performance Optimization

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

THE ROLE: We are looking for a Principal Machine Learning Engineer to join our Models and Applications team. If you are excited by the challenge of distributed training of large models on a large number of GPUs, and if you are passionate about improving training efficiency while innovating and generating new ideas, then this role is for you. You will be part of a world class team focused on addressing the challenge of training generative AI at scale

THE PERSON: The ideal candidate should have experience with distributed training pipelines, be knowledgeable in distributed training algorithms (Data Parallel, Tensor Parallel, Pipeline Parallel, Expert Parallel ZeRO), and be familiar with training large models at scale. KEY RESPONSIBILITIES: Train large models to convergence on AMD GPUs at scale. Improve the end-to-end training pipeline performance

Optimize the distributed training pipeline and algorithm to scale out. Contribute your changes to open source. Stay up-to-date with the latest training algorithms

Influence the direction of AMD AI platform. Collaborate across teams with various groups and stakeholders. PREFERRED EXPERIENCE: Experience with ML/DL frameworks such as PyTorch, JAX, or TensorFlow

Experience with distributed training and dist

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