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General Summary:
We are looking for an engineer to build AI-driven solutions that improve and automate RTL front-end design and implementation flows. The focus is on using agentic AI frameworks and modern open-source tooling to streamline common RTL-to-GDS workflow tasks and reduce manual effort.
Key Responsibilities
Develop AI-based tools/agents to optimize RTL design and front-end flow execution, including automation around analysis, checks, and flow tasks.
Build and maintain agentic workflows using modern open-source ecosystems (e.g., LangChain/CrewAI or similar frameworks).
Create and improve automation and scripting that integrates with design tools and existing engineering workflows.
Write, review, and maintain Python code with good engineering practices (readability, testability, maintainability).
Quickly learn and adopt new AI packages and evolving best practices, as the ecosystem changes rapidly.
Required Qualifications
Strong hands-on experience with Python scripting/automation (comfort writing + reviewing code).
Basic understanding of VLSI RTL → GDS flow and common front-end stages (RTL/design checks/synthesis/STA/CDC/formal/DV, etc.).
Practical familiarity with agentic AI frameworks and tooling (LangChain, CrewAI, LangGraph, or similar open-source stacks).
Ability to work independently, iterate quickly, and learn new tools/packages fast.
Nice to Have
Hands-on experience applying LLM-driven techniques and solutions on large, production-quality SystemVerilog codebases
Exposure to building multi-agent systems (supervisor/worker patterns, tool-calling, memory/context, RAG).
Experience integrating automation with engineering infrastructure and reference flows
Minimum Qualifications:
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