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Join Apple's Silicon Engineering Group to work on next-generation systems-on-chip by converting design concepts into silicon for Analog/Mixed-Signal circuits. This role focuses on automation and leveraging AI/ML techniques for improved design efficiency.
Responsibilities
- Convert design concepts into silicon by collaborating with circuit designers and using advanced CAD tools to create high-performance analog and mixed-signal layouts
- Review floor plans, analyze circuits, run verification suites, and resolve LVS/DRC/ERC issues to deliver next-generation SOCs
- Drive automation by developing scripts, improving workflows, and applying AI/ML for layout optimization, verification acceleration, advanced modeling, intelligent quality metrics, reduced manual effort, and data-driven design insights
Nice to Have
- Experience designing analog and mixed-signal layouts in deep-submicron CMOS and FinFET technologies
- Strong programming/scripting skills in SKILL, Perl, TCL, Shell, and/or Python, with emphasis on layout automation, productivity tooling, and data analysis
- Solid understanding of supervised and self-supervised learning, convolutional neural networks (CNNs), and core training concepts such as loss functions, backpropagation, and optimization
- Hands-on experience with PyTorch for model development and experimentation
- Demonstrated ability to automate layout tasks, flows, and quality checks using scripting and CAD tools
- Proven record in implementing high-quality analog layouts with tight device matching, low noise, and low power consumption
- Familiarity with failure-prone circuit/layout structures, analog and DFM best practices, and the ability to diagnose and resolve layout-related issues
- Strong proficiency in custom and standard-cell floor planning, hierarchical layout assembly, and physical integration
- Understanding of image processing fundamentals, spatial representations, multi-channel image handling, segmentation, and object detection concepts
- Technical understanding of IR drop, RC delay, electromigration, self-heating, coupling capacitance, and related reliability concerns
- High proficiency in interpreting physical verification reports, including DRC, ERC, and LVS
- Experience using advanced Cadence Virtuoso features (XL, EAD, APR, Constraint Manager), and familiarity with Cadence Innovus
- Experience with CAD automation workflows and PCell creation
- Excellent communication skills and the ability to work effectively with cross-functional teams
Skills
AI/ML
*
Python
*
PyTorch
*
Tcl
*
Perl
*
Shell
*
Cadence Innovus
*
SKILL
*
Supervised Learning
*
Self-Supervised Learning
*
Convolutional Neural Networks (CNNs)
*
Cadence Virtuoso
*
* Required skills
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