Senior Principal Applied ML Engineer

Overview

Cadence designs and develops leadership-driven innovation in the world of technology. We focus on advancing chip design and verification through AI-driven automation and data-driven workflows, combining software, ML, and engineering rigor to create state-of-the-art EDA platforms.

Cadence Design Systems is the leading provider of design automation tools for electronic and intelligent systems design. The ML / Software Engineer - Chip Stack Super Agent Team will design, implement, and evaluate AI agents that enhance productivity across the semiconductor design lifecycle. The engineer will contribute to robust agent infrastructure, evaluation systems, and production-grade AI capabilities integrated within Cadence’s EDA ecosystem.

The role focuses on building reliable, scalable agentic systems that operate within complex engineering workflows. The ideal candidate combines strong software engineering fundamentals with practical experience in ML systems and agent infrastructure, enabling deployment of high-impact AI solutions in production environments. You will be part of the Chip Stack AI Super Agent team, collaborating with ML engineers experienced in training large language models at scale and software engineers with product development expertise.

You will work on an agentic AI platform that autonomously designs and verifies chips with potential productivity gains up to 10×.

• Design and implement scalable infrastructure for AI agents operating within Cadence’s Chip Stack Super Agent ecosystem.
• Build robust evaluation frameworks to measure agent performance, reliability, and alignment with engineering workflows.
• Develop data pipelines, retrieval systems, and context-engineering strategies to support grounded agent behavior.
• Contribute to continuous integration, automated testing, and observability systems for production-quality deployment of AI-enabled systems.
• Optimize system performance across latency, cost, reliability, and scalability dimensions.

• BS with a minimum of 10 years of experience OR MS with a minimum of 7 years of experience OR PhD with a minimum of 5 years of experience
• Strong software engineering fundamentals, including design, refactoring, debugging, and testing of complex distributed systems; demonstrated experience building production-quality systems.
• Understanding of large language models (LLMs) and practical considerations for deploying them in real-world systems (latency, cost, reliability, monitoring).
• Experience designing evaluation frameworks for AI systems, including benchmarking, regression testing, and failure analysis.

• Agent architecture:
  Experience with reason-act loops, planning/evaluation/self-correction patterns, tool/function calling, persistent memory systems, and structured outputs.
• LLM engineering:
  Familiarity with frontier LLMs and trade-offs across model families; experience with prompt engineering, context management, and alignment techniques.
• Retrieval and data systems:
  Understanding of RAG pipelines, embeddings, indexing strategies, chunking methodologies, and grounding techniques.
• Infrastructure and observability:
  Experience building logging, tracing, monitoring, and evaluation systems for ML/AI applications.
• AI-assisted development workflows:
  Leveraging AI tools to enhance engineering productivity and code quality.
• Interest in semiconductor design, EDA workflows, and high-performance computing environments.

• Challenge the status quo:
  We are innovators who challenge industry norms and push forward our vision of how silicon should be built.
• Strong opinions, loosely held:
  We are low on ego, but high on collaboration. We are okay to be wrong and are always open to learning.
• Ship fast, ship quality:
  We prioritize what matters, building at high speed without compromising the standards required by the semiconductor industry.
• Proud of our craft:
  Attention to detail is in our DNA.