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Tier-1 employer, metro location, and mid-level title increase competition, though EDA specialization narrows candidate pool.
Strong VLSI/Physical Design and EDA focus makes skills less transferable across industries.
Explicit 2–6 years requirement plus mandatory EDA/Physical Design expertise and technical triage skills restricts candidate pool.
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Own and troubleshoot end-to-end workflows involving Physical Design EDA tools and GenAI/Agentic AI platforms in VLSI/EDA domain.
Drive adoption and scalability of internal AI/GenAI tools by configuring projects, onboarding users, and defining reproducible debugging and test cases.
Collaborate with software engineering teams to improve tool stability, observability, and automate validation pipelines with telemetry data for continuous improvement.
2 to 6 years in Applications Engineering / Customer Engineering, preferably in Physical Design (PNR/Floor planning).
Strong domain knowledge in VLSI / Physical Design / EDA workflows with proven expertise in triaging and debugging complex tool/software issues.
Proficient in Python/Linux and scripting with ability to independently run and debug workflows.
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field with minimum 2 years software engineering or related experience OR Master's with 1+ year OR PhD; plus 2+ years programming experience (C, C++, Java, Python, etc).
Experienced in customer-facing engineering roles involving deep debugging and triage of complex EDA and AI-integrated workflows.
Comfortable working at the intersection of Physical Design engineering, software teams, and AI/GenAI agent orchestration with hands-on tool usage.
Capable of defining functional specifications, test strategies, and using objective metrics (like timing slack and congestion) to measure tool performance and reliability.