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# Applied ML Director (Technical Team Lead)

- **Company:** Cadence Design Systems, Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $178,500.0 - $331,500.0
- **Contract:** Permanent contract
- **Skills:** Adobe InDesign, Artificial Intelligence, Automated Storage and Retrieval Systems, Automation of Tests, Computer Engineering, Continuous Integration, Data Systems, Software Debugging, Distributed Systems, Monitoring of Systems, Machine Learning, Regression Testing, Software Engineering, System Testing, Data Logging, High Performance Computing, Retrieval-Augmented Generation, Large Language Models, Database Optimization, Prompt Engineering, Agentic-AI, Information Technology, Invoking Functions, Code Restructuring, Data Pipelines
- **Published:** October 26, 2026
- **Apply:** https://www.sanjosejobsite.com/job.asp?id=3466804583&tx=KL8787FFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Education: MS or PhD in Computer Science, Computer Engineering, or a related technical field.
 * Leadership Experience: 3+ years of direct engineering management or formal technical lead experience, with a proven track record of successfully mentoring engineers and delivering complex projects.
 * Engineering Fundamentals: 7+ years of hands-on software engineering and ML experience. You must possess deep expertise in 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 methodologies, and grounding techniques.
 * Infrastructure & Observability: Experience building logging, tracing, monitoring, and evaluation systems specifically tailored for ML/AI applications.
 * Domain Interest: A strong interest in semiconductor design, EDA workflows, and high-performance computing environments (prior EDA experience is a plus, but not required ).

Our Culture

 * 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 ruthlessly prioritize what matters. We build at lightning speed, but never compromise on the high standards required by the semiconductor industry.
 * Proud of our craft: Attention to detail is in our DNA. We take pride in what we build and go the extra mile to ensure an exceptional experience for our users.

## Description

* 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 the ChipStack SuperAgent ecosystem.
 * Architect Production AI: Guide the 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 to optimize system 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.