AI and Automation Software Engineer

AMD AMD · Semiconductors · MARKHAM, Canada · Engineering

Software Engineer to lead AI and Automation innovation within the NBIO Team, focusing on developing and deploying AI-enabled tools to increase post-silicon team efficiency, accelerate engineering execution, and reduce manual work in silicon verification, validation, and debug workflows.

What you'd actually do

  1. Identify and prioritize opportunities where AI, automation, scripting, data processing, or workflow integration can materially improve engineering productivity and team efficiency.
  2. 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.
  3. Design, develop, test, and deploy automation solutions using Python and other relevant programming languages.
  4. Continuous Integration/Continuous Deployment (CI/CD): Implement and maintain CI/CD pipelines to automate software delivery processes and ensure efficient deployment.
  5. Implement AI/ML framework (i.e.: LangChain, LaMDA) and concepts into software applications to enhance functionality and user experience.

Skills

Required

  • Python
  • AI/ML framework implementation (e.g., LangChain)
  • CI/CD pipeline implementation
  • Collaboration with frontend and backend developers
  • Technical documentation creation
  • Understanding of engineering pain points
  • Automation asset development
  • Defining success criteria for automation

Nice to have

  • Semiconductor industry experience
  • Post-silicon validation methodologies
  • PCIe, CXL, xGMI, UALink, SoC platform bring-up, firmware debug, silicon validation, or server platform execution
  • Cloud platforms (AWS, Azure, GCP)
  • Containerization and orchestration (Docker, Kubernetes)
  • Python, C, C++, JavaScript proficiency
  • CI/CD tools (Jenkins, Github Actions)
  • Version control systems (Git)
  • Testing frameworks (pytest)
  • Frontend technologies (HTML, CSS, JavaScript, React, Angular)
  • Backend development (SQL, NoSQL, Node.js)

What the JD emphasized

  • proven track record of identifying high-impact productivity gaps
  • delivering AI-enabled tools that are adopted by engineering teams and produce measurable improvements
  • translate real engineering pain points into practical AI/automation solutions
  • Convert manual and fragmented engineering workflows into reusable, scalable, and well-documented automation assets
  • Define measurable success criteria for automation initiatives

Other signals

  • Develop AI-enabled tools for engineering productivity
  • Streamline repetitive tasks using AI and automation
  • Implement AI/ML frameworks like LangChain