CAD ML Timing Optimization Engineer – 200569926 -Austin, Texas, United States

Apple

Do you love creating elegant solutions to highly complex challenges? Do you intrinsically see the importance in every detail? As part of our Silicon Technologies group, you’ll help design and manufacture our next-generation, high-performance, power-efficient processor and system-on-chip (SoC). You’ll ensure Apple products and services can seamlessly and efficiently handle the tasks that make them beloved by millions. Joining this group means you’ll be responsible for crafting and building the technology that fuels Apple’s devices. We invite you to help deliver the next groundbreaking Apple products!

In this highly visible role as a key technical member of the Design Methodology and Tools team, you are an integral part of the effort to improve the performance of Apple silicon. You will be responsible for delivering industry-leading solutions for design optimization, design closure, and visualization. Combining machine learning algorithm application with practical design know-how and software engineering best practices, you will help to differentiate and streamline Apple’s silicon engineering methods.

As a CAD ML Timing Optimization Engineer, you will:

– Deliver methodology and tool solutions for static timing closure and power optimization.

– Apply data science and ML analytics to quantify, mine, and predict intriguing patterns.

– Deploy innovative modeling and optimization approaches to achieve globally optimal targets.

– Prudently apply best-in-class learning algorithms to deliver value-adding design solutions.

– Pursue deep analysis of design implementation alternatives to isolate key issues and identify appropriate ECO remedies.

– Implement code infrastructure to facilitate analytics and visualization.

– Collaborate with silicon design, CAD, and EDA partners to identify flow deficiencies and enact creative solutions.

Minimum BS and 10+ years of relevant industry experience.Software engineering background using Python and C++.Experience in applying ML and/or GPU accelerated approaches to problems.Prior usage of optimization algorithms, data modeling, and graphs.

 

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