AI and Automation Software Engineer

Advanced Micro Devices, Inc•Markham, ON

14h•Hybrid

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them. AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward. THE ROLE: The AMD NBIO Team is on the lookout for a seasoned Software Engineer to lead our AI and Automation innovation. As a key contributor to the success of AMD’s IP, you will be leading the design, development, deployment and maintenance of game-changing AI and automation tools that significantly increase the post silicon team efficiency, accelerate engineering execution, and reduce repetitive manual work across silicon verification, validation, debug, and program delivery workflows.

Requirements

Bachelor’s or Master’s degree majoring in EE, CS or related field.

Nice To Haves

Experience working in semiconductor industry, familiar with post-silicon validation methodologies
Background in PCIe, CXL, xGMI, UALink, SoC platform bring-up, firmware debug, silicon validation, or server platform execution is a plus.
Familiar with AI/ML concepts and their practical applications in hardware and software development.
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud Platform).
Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
Certification in relevant technologies or frameworks (e.g., AWS Certified Developer, TensorFlow Developer Certificate).
Proficiency with one or more of the following languages: Python, C, C++ programming, JavaScript.
Experience with CI/CD tools (e.g., Jenkins, Github Actions) and version control systems (e.g., Git).
Familiarity with testing frameworks (e.g., pytest) and automated testing practices.
Familiarity with frontend technologies such as HTML, CSS, JavaScript, and frontend frameworks (e.g., React, Angular).
Experience with backend development using databases (e.g., SQL, NoSQL) and backend frameworks (e.g., Node.js).
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration abilities.

Responsibilities

Identify and prioritize opportunities where AI, automation, scripting, data processing, or workflow integration can materially improve engineering productivity and team efficiency.
Develop game-changing AI and automation tools that streamline repetitive tasks such as log analysis, experiment/result summarization, debug triage, issue tracking, reporting, documentation, and cross-team status consolidation.
Design, develop, test, and deploy automation solutions using Python and other relevant programming languages.
Continuous Integration/Continuous Deployment (CI/CD): Implement and maintain CI/CD pipelines to automate software delivery processes and ensure efficient deployment.
Collaborate with frontend and backend developers to create seamless user interfaces and robust backend systems.
Implement AI/ML framework (i.e.: LangChain, LaMDA) and concepts into software applications to enhance functionality and user experience.
Create and maintain technical documentation for software solutions, including code documentation (ie: Doxygen, Sphinx) and user guides.
Partner with design, verification, validation, firmware and platform stakeholders to translate real engineering pain points into practical AI/automation solutions.
Convert manual and fragmented engineering workflows into reusable, scalable, and well-documented automation assets that can be adopted across teams.
Define measurable success criteria for automation initiatives, including hours saved, faster triage, reduced manual handoffs, improved reporting consistency, and shorter debug or execution cycles.
Ensure AI-generated outputs are reviewable, traceable, technically grounded, and aligned with responsible AI, confidentiality, and data-handling expectations