Physical Design Engineer, Machine Learning

Summary: At Apple, we believe our products begin with our people. By hiring a diverse team, we drive creative thought. By giving that team everything they need, we drive innovation. By hiring incredible engineers, we drive precision. And through our collaborative process, we build memorable experiences for our customers! These elements come together to make Apple an amazing environment for motivated people to do the greatest work of their lives. You will become part of a hands-on development team that sets the standard in cultivating excellence, creativity, and innovation. Come help us design the next generation of revolutionary Apple products. We are looking for a forward-thinking and unusually talented engineer. As a member of our dynamic 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 directly involved in our physical design machine learning efforts, collaborating right alongside our internal multi-functional teams, and using your expertise in machine learning and physical design to ensure that our SOCs achieve the optimal Power, Performance, and Area (PPA). We account for every nano watt, every nano meter, and every pico second.Description: As a member of the physical design machine learning architecture team, 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 machine learning to solve very hard and unique problems.Your work will directly impact vast areas of the flow including RTL design, 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.You will collaborate cross-functionally with design, power, post silicon, CAD, software, and machine learning teams in an engaging and rewarding environment.Minimum Qualifications:Minimum BS and 3+ years of relevant industry experience.Understanding of optimization algorithms, complex data structures, and linear algebra.Understanding of VLSI fundamentals, including physical design.Key Qualifications Preferred Qualifications:Practical experience and knowledge in advanced machine learning algorithms like GNNs, VAEs, transformers, diffusion models, LLMs.Excellent programming skills in Python and C/C++.Master's or PhD with relevant publications in Machine Learning and/or EDA algorithms.Excellent communication and organizational skills.Education & Experience Additional Requirements:Pay & Benefits:At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $143,100 and $264,200, and your base pay will depend on your skills, qualifications, experience, and location.Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.

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