Software Engineer (Circuit Analysis, EDA frameworks, AI agents)

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Cadence Design Systems is the leading provider of design automation tools for electronic and intelligent systems design. The Circuit Analysis Engineer is responsible for designing, implementing, and maintaining software used for transistor-level electrical circuit simulation in the epoch of emerging Agentic AI frameworks. Engineer will be part of a team focused on modernizing VLSI circuit simulation tools to integrate into emerging Agentic AI frameworks in the broader electronic design automation context.

Responsibilities

Design and implement new software features and interfaces in the Cadence circuit simulation tool portfolio.

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 in a geographically distributed environment 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++.

Candidate does not have to be an ML / AI researcher or AI specialist to be successful in this role, but must 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, CI / CD automation, and 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, background in ML, data pipelines, predictive modeling, and deployment. Practical knowledge of frontier LLMs (e.g., GPT-5 / Claude / Gemini 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), MCP, 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.

Familiarity with electronic design automation tools used for design of VLSI circuits is a plus.

Familiarity with VLSI circuit simulation tools (Spice, Spectre) is a plus.

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