Applied ML Director; Technical Team Lead

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

About Us

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-artEDA software and hardware platforms (includingXcelium, Jasper, Palladium, Protium, and Helium) and supercharge them with cutting-edgeAI, 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. As the Applied ML Director for theChip Stack Super Agent Team , you will lead a highly technical group of ML and software engineers responsible for designing, implementing, and evaluating AI agents that enhance productivity across the semiconductor design lifecycle.

This is a true “player-coach” role. You will act as the technical backbone of the team—deeply hands-on with architecture, system design, and coding—while concurrently managing, mentoring, and scaling the engineering team. You will drive the technical roadmap for our agent infrastructure, evaluation systems, and production-grade AI capabilities integrated within Cadence’s EDA ecosystem. The ideal candidate pairs seasoned engineering leadership with practical, in-the-weeds experience building scalable ML systems and agentic workflows.

Responsibilities

• Lead & Mentor: Manage and grow a high-performing team of ML and software engineers. Foster a culture of technical excellence, continuous learning, and rapid execution.
• Hands-On Technical Leadership: Drive the technical vision and actively contribute to the codebase. Design, implement, and review scalable infrastructure for AI agents within theChip Stack Super Agentecosystem .
• Architect Production AI: Guidethe development of robust evaluation frameworks, data pipelines, retrieval systems (RAG), and context-engineering strategies to ensure consistent, grounded, and aligned agent behavior.
• Operational Excellence: Oversee continuous integration, automated testing, and observability systems. Make high-level architectural decisions tooptimizesystem performance across latency, cost, reliability, and scalability.
• Cross-Functional Collaboration: Partner with product management, research, and core engineering teams to align the AI roadmap with overarching EDA platform goals.

Required Qualifications

• Education: MS or PhD in Computer Science, Computer Engineering, ora related technical field.
• Leadership
  
  Experience:
  
  3+ years of direct engineering management or formal technical lead experience, with a proven track recordof successfully mentoring engineers and delivering complex projects.
• Engineering Fundamentals: 7+ years of hands-on software engineering and ML experience. You mustpossessdeepexpertisein design, refactoring, debugging, and testing distributed systems—and you should still be comfortable writing production-quality code today.
• LLM Expertise: Deep understanding of large language models (LLMs) and the practical realities of deploying them in production (latency, cost, reliability, monitoring, and failure analysis).
• System Evaluation: Experience designing rigorous evaluation frameworks for AI systems, including benchmarking and regression testing.

Skills of Interest

• Agent Architecture: Hands-on experience with reason–act loops, planning/self-correction patterns, tool/function calling, persistent memory systems, and structured outputs.
• LLM Engineering: Familiarity with frontier LLMs and trade-offs across model families; practical experience with prompt engineering, context management, and model alignment techniques.
• Retrieval & Data Systems: Deep understanding of RAG pipelines, embeddings, indexing strategies, chunking…