Physical Design Engineer, Machine Learning

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: San Jose, CA
Salary: $150,400–$277,600 / yr
Posted: 15 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $214k

This role $214k

$135k most similar roles pay here $293k

This role pays more than 57% of similar roles. Most pay $181,795–$246,150 — the shaded band above. At the midpoint, this role pays about $214k versus about $214k for comparable roles.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 2925 open roles on FindRole.

Listed pay typically runs $166,600–$277,600 across 2305 roles with salary data.

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At a glance

TL;DR · Physical Design Engineer, Machine Learning

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As a Physical Design Engineer, Machine Learning, you will join the Physical Design Machine Learning team to develop advanced solutions for System on Chip (SOC) optimization. You will build predictive models, optimization algorithms, and autonomous agents designed to improve Power, Performance, and Area (PPA). Your daily work involves applying machine learning to solve complex problems across the physical design flow, including RTL synthesis, floorplanning, place and route, timing analysis, and manufacturing yield. You will train and deploy models into production flows while developing agentic systems for automated optimization loops. The role requires proficiency in Python or C/C++, as well as experience with GNNs, reinforcement learning, transformers, and diffusion models. You will also utilize TCL and APIs to integrate these machine learning tools directly into existing EDA tool flows.

Skills

Machine Learning Python C++ TCL GNNs Reinforcement Learning Diffusion Models Physical Design Floorplanning Place and Route Optimization Algorithms Agentic Systems Multi-agent Orchestration

What you'll do

Apply machine learning and advanced algorithms to solve problems across the physical design flow including floorplanning, place and route, and timing analysis.
Train and deploy models directly into production P&R flows to predict outcomes and accelerate convergence.
Build tools and models for agentic systems to automate optimization loops that propose and evaluate design changes.
Develop autonomous or semi-assisted optimization loops that iterate on designs through EDA tooling.
Implement a wide range of ML techniques including GNNs, reinforcement learning, and LLM-based agents.
Integrate machine learning models into EDA tool flows using Python, TCL scripting, or APIs.
Optimize SoC Power, Performance, and Area (PPA) through predictive modeling and automated optimization algorithms.

What we're looking for

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

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