AI Design Automation Engineer

AMD is a semiconductor technology company advancing processors, AI technologies, and related products. The AI Design Automation Engineer will build AI-powered engineering platforms, scalable software systems, data pipelines, knowledge graphs, and LLM-driven capabilities for semiconductor design teams. The role will expand platform support for engineering workflows and agent-based experiences that accelerate chip development.

Responsibilities

• 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

Preferred

• Strong Python engineering background, with rigorous testing discipline (unit/integration/end-to-end) in a large, shared codebase
• Experience with graph databases and/or vector search systems, including schema design and query performance for retrieval workloads
• Experience with LLM-based/agentic systems: retrieval-augmented generation, agent orchestration frameworks, tool-use/function-calling, and awareness of LLM cost/latency/token tradeoffs
• Familiarity with RTL/HDL/Physical Design semantics (SystemVerilog/Verilog/SW/HW modeling, module hierarchy, instantiation, parameters) and toolchains for parsing/elaborating hardware design source
• Experience with time-series/relational data stores and reasoning about consistency across multiple database backends
• Strong written and verbal communication skills
• Python Engineering
• Unit Testing
• Integration Testing
• End-to-End Testing
• Large Shared Codebases
• Graph Databases
• Vector Search
• Schema Design
• Query Performance Optimization
• Retrieval Workloads
• LLM-Based Systems
• Agentic Systems
• Retrieval-Augmented Generation
• Agent Orchestration Frameworks
• Function Calling
• LLM Cost Optimization
• LLM Latency Tradeoffs
• Token Usage Tradeoffs
• RTL/HDL Semantics
• Physical Design Semantics
• SystemVerilog
• Verilog
• Software/Hardware Modeling
• Module Hierarchy
• Hardware Instantiation
• Hardware Parameters
• Hardware Source Parsing
• Hardware Source Elaboration
• Time-Series Data Stores
• Relational Data Stores
• Cross-Database Consistency
• Written Communication
• Verbal Communication

Company

• High-performance computing solutions for data centers, AI, PCs, gaming
• Santa Clara, California, USA
• http://www.amd.com/
• Founded in 1969
• 10001+ employees

Qualifications

• 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
• ACADEMIC CREDENTIALS: Bachelors/Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• This role is not eligible for visa sponsorship
• Strong Python engineering background, with rigorous testing discipline (unit/integration/end-to-end) in a large, shared codebase
• Experience with graph databases and/or vector search systems, including schema design and query performance for retrieval workloads
• Experience with LLM-based/agentic systems: retrieval-augmented generation, agent orchestration frameworks, tool-use/function-calling, and awareness of LLM cost/latency/token tradeoffs
• Familiarity with RTL/HDL/Physical Design semantics (SystemVerilog/Verilog/SW/HW modeling, module hierarchy, instantiation, parameters) and toolchains for parsing/elaborating hardware design source
• Experience with time-series/relational data stores and reasoning about consistency across multiple database backends
• Strong written and verbal communication skills

Required skills

• Data Pipelines, Graph Databases, LLM Agent Orchestration, Python, RTL/HDL Design Semantics, Relational Databases, Retrieval-Augmented Generation, Software Testing, SystemVerilog/Verilog, Time-Series Databases, Tool Use and Function Calling, Vector Search

Preferred skills

• $4.75B, 2023-03-02, 2025-10-06, 2026-05-14, 2026-10-23, 4.1, AI Tools, BetaKit, CFO, Chair & CEO, Company data provided by, Customize Your Resume, Daniel Loeb, Glassdoor, Jean Hu, Key Investors, Leadership Team, Lisa Su, Maximize your interview chances, OpenAI, Post Ipo Debt· $4.75B, Post Ipo Equity, Public Company, Recent News, Santa Clara, California, USA, Strong written and verbal communication skills, The Motley Fool, Total Funding, Why AMD Stock Fell on Thursday, http://www.amd.com

Experience

• Mid level