Physical Design Engineer - Machine Learning

Summary

Come help us design the next generation of revolutionary Apple products. We are looking for an engineer who combines deep physical design expertise with hands-on machine learning skills. In this role, you will work on our physical design machine learning efforts — building predictive models, optimization algorithms, and autonomous agents that collaborate with our internal design teams to help our SOCs achieve optimal Power, Performance, and Area (PPA).

Minimum Qualifications

Minimum BS and 3+ years of relevant industry experience Experience with optimization algorithms and programming in Python or C/C++ Academic or industry experience in physical design

Preferred Qualifications

Practical experience with a range of ML approaches including classical/traditional models, GNNs, transformers, diffusion models, and/or reinforcement learning Experience building agentic systems, LLM-based agents, tool-calling/function-calling, multi-agent orchestration, or autonomous decision-making loops Experience integrating ML models or agents into EDA tool flows via scripting (Python/TCL) or APIs Master's or PhD with relevant publications in Machine Learning and/or EDA algorithms Excellent communication and organizational skills

Description

As a member of the Physical Design Machine Learning team, you will help build the most efficient application processors on the planet, powering the next generation of Apple products. Job responsibilities include: • Applying machine learning and advanced algorithms to solve hard, high-impact problems across the physical design flow: RTL and logic synthesis, floorplanning, place and route, timing/noise/power/thermal analysis, voltage drop, and design for manufacturing/yield • Training and deploying models directly into production P&R flows to predict and optimize outcomes and speed up convergence • Building tools and models designed to be used by agentic systems, as well as agents themselves, including autonomous or semi-assisted optimization loops that propose, evaluate, and iterate on design changes through EDA tooling • Working across the full spectrum of ML techniques, from traditional models and classical optimization to GNNs, reinforcement learning, and LLM-based agents • Collaborating cross-functionally with design, power, post-silicon, CAD, software, and machine learning teams in an engaging and rewarding environment