Design Automation Engineer - AMD

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.

Whether you're designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we'll advance your career.

As an AMD-AECG Design Automation Engineer, you will help build the next generation of AI-powered engineering platforms that enable AMD's chip design teams to find, understand, and interact with complex semiconductor design data. You will develop scalable software systems, data pipelines, knowledge graphs, and AI/LLM-driven capabilities that transform how architects, designers, and verification engineers access and leverage engineering knowledge. Working at the intersection of AI, software infrastructure, and semiconductor design, you will help expand the platform to support new engineering domains, workflows, and agent-based experiences that accelerate chip development.

The ideal candidate is a strong software engineer with experience building production-quality backend systems and a passion for applying AI to solve complex technical problems. You are comfortable designing data models, building large-scale data pipelines, and developing AI/LLM-based solutions such as retrieval systems, knowledge graphs, and agentic workflows. You thrive in highly technical and ambiguous environments, can collaborate effectively with both software and hardware engineers, and are motivated by the opportunity to build tools that directly impact the design of next-generation CPUs, GPUs, and AI accelerators.

• Design, build, and maintain backend data pipelines that turn engineering data sources into structured, quarriable representations
• Design and evolve data schemas and queries to support new engineering data types and use cases at production scale
• Build and extend AI/LLM-based components that let engineers query and interact with engineering data using natural language
• Diagnose and fix reliability and performance issues in distributed batch-processing pipelines handling large volumes of data
• Design and implement new data pipelines to bring additional engineering data domains onto the platform
• Tune LLM-based components for accuracy, cost, and latency
• Own a TDD-driven development workflow using the team's version control and code review practices
• Collaborate with architecture, verification, and design teams to translate their data and workflow needs into platform capabilities