Lead Software Engineer - AI/LLM for Virtuoso

What You'll Do

• · Design and implement new software features and interfaces in the Cadence Virtuoso platform. · Contribute to continuous integration, unit testing, and code quality processes using modern C++ paradigms and AI-assisted development workflows. · Collaborate with cross-functional engineering teams across time zones (US, China, EU, UK, and India) to deliver production-quality simulation technologies.
• Required Qualifications · MS/PhD in Computer Science/Computer Engineering. · Strong software engineering fundamentals, including design, refactoring, debugging, and testing of complex applications.
• Candidate should be familiar with test-driven development and design of componentized, modular software using minimalist design principles.
• Candidate should have demonstrated history of both new software development and ability to refactor existing code bases. · Proficient in Python, C++. · Proficient in English (written and verbal) for cross-site collaboration. · Candidate need to have the necessary computer science background to be able to design and deploy production software systems that make use of ML/LLM technology.
• You will not be developing new ML models themselves, but innovating to use AI technology in an EDA software framework and pushing the boundaries of Agentic AI within those frameworks. · Desire to develop and deploy next-generation production EDA tools that have real-world impact on productivity of integrated circuit designers.
• Skills of Interest · Software quality practices: Experience with unit testing, code review processes. · AI-assisted development workflows: Ability to integrate AI tools into engineering workflows to enhance productivity and code quality. · Machine Learning Engineering: Basic background in machine learning, data pipelines, predictive modeling, and deployment.
• Practical knowledge of frontier LLMs (e.g., DeepSeek, Qwen, GPT/Claude families) and how choices affect latency, cost, and reliability. · Agent architecture: Familiarity with concepts such as ReAct (reason-act loops), planning/evaluation/self-correction, and persistent memory design; frameworks (e.g., LangChain, Dify, Coze); MCP (Model Context Protocol), function/tool calling, and structured outputs. · Data and retrieval: Understanding of RAG, retrieval pipelines, embeddings, chunking, and grounding. · Context engineering: Designing structured context to produce consistent, predictable outputs despite changing LLM behavior. · Regulatory awareness: Familiarity with China's AI governance framework and data privacy regulations (PIPL) is a plus. · Familiarity with electronic design automation tools used for design of analog circuits is preferred.
• We’re doing work that matters.
• Help us solve what others can’t.