Architect and develop AI-driven solutions for static timing, constraints quality, and closure prediction.
Integrate heterogeneous data sources — timing reports, constraint graphs, design metadata, silicon correlation — into structured knowledge bases and training pipelines.
Develop autonomous analysis agents that interact with timing tools (e.g., PrimeTime, Nanotime, Tempus) to perform multi-corner, multi-mode optimization and constraint debugging.
Implement scalable orchestration across Flow-Server and Digital Engineer platforms, enabling AI-in-loop decision-making for sign-off readiness.
Collaborate with methodology and sign-off teams to validate models on live projects, improving coverage, predictability, and engineering productivity.
Build interpretable AI pipelines using graph neural networks, large language models, and process-aware reasoning engines for timing closure recommendations.
Be responsible for the end-to-end lifecycle — from data curation and model training to deployment, monitoring, and continuous improvement in production environments.

Requirements

BS (or equivalent experience) in Electrical or Computer Engineering with 12+ years of experience in AI/ML solution development, ideally for EDA, semiconductor, or complex data domains
Strong background in VLSI/ASIC design — with deep understanding of timing, constraints, STA, or sign-off workflows.
Proficiency in Python, PyTorch/TensorFlow, and graph or agentic AI frameworks (e.g., LangGraph, LangChain, Ray, NetworkX).
Experience developing data pipelines, knowledge graphs, or process models for structured engineering data.
Working knowledge of timing tools (PrimeTime, Nanotime, Tempus) and scripting integration with EDA environments.
Experience with AI orchestration frameworks, reasoning based on prompts, and multi-agent automation is highly desirable.
Strong problem-solving skills, technical depth, and a mentality for experimentation and continuous learning.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

AI-driven solutionstiming analysisconstraints qualityclosure predictiondata pipelinesknowledge graphsgraph neural networkslarge language modelsPythonVLSI/ASIC design

Soft Skills

problem-solvingtechnical depthexperimentationcontinuous learning