Physical Design Engineer, Machine Learning
Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Oct 26, 2026. Apple scores A on the Alion truth index.
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).
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
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