AI Performance Architect

Job Description

Who We Are Applied Materials is the global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world like AI and IoT. If you want to work beyond the cutting-edge, continuously pushing the boundaries ofscience and engineering to make possiblethe next generations of technology, join us to Make Possible a Better Future. At Applied, we prioritize your well-being and encourage you to bring your best self to work. Your happiness, health, and resiliency are at the core of our benefits and wellness programs. Our robust total rewards package makes it easier to take care of your whole self and your whole family. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Applied Materials' Applied AI Systems Solutions (System to Materials) Business Unit is searching for a Software Engineer AI Performance Architect to join our team! The Applied AI System to Materials team works on architecting differentiated AI Systems leveraging Applied's fundamental innovations. Write the details of role here: Benchmark AI workloads (LLMs) in single and multi-node High Performance GPU configurations. Project and Analyze systems performance for LLMs using various parallelization techniques. Develop methodologies to measure key performance metrics and understand bottlenecks to improve efficiency. Requirements Understanding of transformer-based model architectures and basic GEMM operations. Strong programming skills in Python, C/C++. Proficiency in systems (CPU, GPU, Memory, or Network) architecture analysis and performance modelling. Experience with parallel computing architectures, interconnect fabrics and AI workloads (Finetuning/Inference). Experience with DL Frameworks (Pytorch, Tensorflow), Profiling tools (Nsight Systems, Nsight Compute, Rocprof), Containerized Environment (Docker)