SoC Physical Design Machine Learning Engineer

Summary

As a member of our dynamic ML PD group, you will have the rare and rewarding opportunity to craft and implement methodologies and solutions with a high impact on upcoming products that will delight and inspire millions of Apple’s customers every single day.

In this role, you will be part of a very unique team that develop new physical design methodologies

using ML, AI and state of the art Algorithms. You will have the opportunity to help each of our SOCs to be even

more optimal in terms of Power, Performance, and Area (PPA) and you will help to improve the RTL to GDS cycle.

Key Qualifications

5+ years of experience in physical design

Excellent programming skills in Python or C ++

Practical experience and knowledge in various machine learning algorithms, from logistic regression to deep neural networks and reinforcement learning is a plus

Excellent communication and organizational skills

Solid math background and understanding of algorithms and data structures

Strong intellectual curiosity

Solid understanding of circuit design is a plus

Experience with flow development for a large number of users on a tight schedule is a plus

Description

As a SoC Physical Design Machine Learning Engineer, you will be part of a dynamic team that is building

the most efficient application processors on the planet, powering the next generation of Apple products.

You will use your experience in physical design and/or machine learning to solve very hard and unique problems.

Your work will directly impact vast areas of the flow including logic synthesis, floor planning, power/clock, distribution, place and route, timing/noise analysis, power/thermal analysis, voltage drop analysis, design for manufacturing/yield, and beyond.

As part of your work, you will collaborate cross functionally with design, power, post silicon, CAD, software and machine learning teams in an engaging and rewarding environment.

Education & Experience

Minimum Bachelor's degree in EE.

Role Number: 200476229