Principal Applied ML Engineer

Cadence Design Systems • San Jose, CA

5d

About The Position

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Chips are at the center of today's tech-driven world. But how we design and verify them has not fundamentally changed in decades, while their complexity and specialization have skyrocketed due to increasing performance demands from AI. We are a dynamic, fast-moving team of software developers, ML scientists, and research-minded engineers on a mission to change that. Operating with the agility of a startup but backed by industry-leading verification technologies, we are part of the System Verification Group (SVG). Our charter is to develop state-of-the-art EDA software and hardware platforms (including Xcelium, Jasper, Palladium, Protium, and Helium) and supercharge them with cutting-edge AI, automation, and advanced data-driven workflows. About This Role Cadence Design Systems is the leading provider of design automation tools for electronic and intelligent systems design. The ML / Software Engineer – ChipStack SuperAgent Team will be responsible for designing, implementing, and evaluating AI agents that enhance productivity across the semiconductor design lifecycle. This engineer will contribute to the development of robust agent infrastructure, evaluation systems, and production-grade AI capabilities integrated within Cadence’s electronic design automation (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. In this role, you will be part of the ChipStack AI Super Agent team and will operate at the forefront of semiconductor design and AI innovation, utilizing advanced AI tools to architect, design, and validate the next generation of verification methodologies. You will collaborate closely with a highly skilled team of machine learning engineers experienced in training large language models at scale, as well as accomplished software engineers with proven expertise in product development and deployment. You will be working on the world’s first agentic AI platform that autonomously designs and verifies chips with up to 10× productivity gains.

Requirements

• BS with a minimum of 7 years of experience OR MS with a minimum of 5 years of experience OR PhD with a minimum of 1 year 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.

Nice To Haves

• 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.

Responsibilities

• Design and implement scalable infrastructure for AI agents operating within Cadence’s ChipStack SuperAgent 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 consistent and grounded agent behavior.
• Contribute to continuous integration, automated testing, and observability systems to ensure production-quality deployment of AI-enabled systems.
• Optimize system performance across latency, cost, reliability, and scalability dimensions.

Benefits

• paid vacation and paid holidays
• 401(k) plan with employer match
• employee stock purchase plan
• a variety of medical, dental and vision plan options

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level