PREFERRED EXPERIENCE:

• Strong python and C/C++ programming experience
• Strong understanding of AI/ML algorithms, tools/framework, and model representations such as onnx, pytorch
• Strong experience in analytical modelling of the ML operators on target architectures in terms of compute and date movement
• Background in using optimization libraries and solvers (e.g., PuLP, CBC, Gurobi) is a plus
• Effective communication and problem-solving 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_

THE ROLE:

We are recruiting a highly qualified senior engineer to join a team which works at the forefront of the rapidly developing AI field with critical impact on the AMD’s AI/ML products. The team’s responsibilities include providing scalable and performant solutions for the efficient mapping and implementation of the ML workloads on the existing and next-generation AMD products, which align with AMD’s software and hardware roadmaps. We are looking for an outstanding engineer with a strong background in the ML usecases, hardware architectures for AI/ML, and analytical modelling of the ML use cases mapped onto target hardware architectures, to provide performance projections for the exploration and enabling of the scalable and optimized solutions on the existing and next-generation architectures.

KEY RESPONSIBILITIES:

• Analytical modelling of the compute and data movement for the performance optimized mapping of the ML use cases onto the parallel processing architectures such as NPU and GPU
• Applying strong software engineering skills to implement/improve performance optimization techniques to be integrated into performance projection tool flows
• Using performance projection tools to provide results for target ML workloads and hardware architectures
• Technical interaction with internal engineering teams and AMD customers
• Stay informed of trends in the areas of ML models/algorithms, software stacks and hardware architectures