Sr. Machine Learning Performance Engineer

at  Advanced Micro Devices Inc

PREFERRED EXPERIENCE

• Strong technical expertise and experience in performance analysis, projection, and ML hardware architecture.
• Experience with software optimization and performance modeling, including modeling workloads such as generative AI models on multiple HW configurations.
• Excellent written, verbal, and presentation skills.

WHAT YOU DO AT AMD CHANGES EVERYTHING

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
AMD together we advance_
Responsibilities:

THE ROLE

We seek a Sr. Machine Learning Performance Engineer to focus on ML Performance modeling, projection, and optimization for various ML workloads, and participate in hardware and software co-design. You will focus on the interaction between ML workloads and hardware architecture, including modeling workloads such as generative AI models on multiple HW configurations and summarize your recommendation. Furthermore, you will be working with customers and business units on perf projection, analysis, and come up with novel solutions to satisfy customer needs.
If you are passionate about performance optimization, getting the best out of the HW, and shaping the future AI acceleration, then this role is for you.

KEY RESPONSIBILITIES

• Performance modeling and analysis of ML training and inference workloads across a single and multiple accelerators. In addition of exploring various tradeoff and design decision.
• Participate in hardware-software co-design for future hardware optimization on various ML workloads.
• Communicate and present the results of the performance analysis and modeling to stakeholders and provide a concrete recommendation.
• Develop and improve our framework, tools and infrastructure for performance estimation, modeling and reporting.
• Cross team collaboration.