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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
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As a Gate-level IR/EM CAD/NLP Engineer within the CAD Group, you will develop an analytic platform to identify and recommend solutions for signoff IR/EM analysis. Your daily responsibilities involve building machine learning and large language model applications to interpret and interact with EDA software, creating customized reports and dashboards to improve power grid integrity, and automating various IR/EM analysis and signoff flows. You will collaborate with design groups and EDA vendors to enhance efficiency and capacity across several silicon design stages. The role requires proficiency in Python, UNIX shell environments, and code revision control, alongside expertise in data structures, algorithms, and NLP techniques like fine-tuning and prompt engineering. This position addresses technical challenges in static/dynamic IR analysis, signal/power grid extraction, STA timing, and signoff for advanced silicon design.
Develop ML and LLM applications to interpret, explain, and interact with EDA software.
Design and implement the integration of these models into existing compute infrastructure.
Create custom reports and dashboards to identify IR/EM issues and recommend power grid solutions.
Automate and streamline various IR/EM analysis and signoff workflows.
Support design groups by addressing their specific requirements for different analysis flows.
Work with EDA vendors to develop solutions that improve tool efficiency and capacity.
What we're looking for
Bachelor of Science degree and at least 3 years of relevant industry experience.
Experience developing object-oriented programs, utilizing data structures, and writing algorithms.
Experience writing machine learning algorithms, implementing natural language processing (NLP), or using large language models in industrial applications.
Experience with EDA tools, CAD flows, or Tool Flow Methodology (TFM).
Proficiency with Python, UNIX shell environment, and code revision control.
Knowledge of PNR implementation and/or EMIR analysis flow (preferred).
Experience in electrical engineering machine learning applications (preferred).
Experience with large language models including fine-tuning, retrieval augmented generation, and prompt engineering (preferred).