AI Verification Engineering Intern

We are looking for a highly motivated AI Verification Engineering Intern to help develop AI-powered solutions for semiconductor verification productivity. You will work closely with verification engineers to build intelligent tools and agents that automate engineering workflows, improve debugging efficiency, and accelerate verification execution.

Responsibilities

• Develop AI-powered verification assistants and engineering agents.
• Build automation flows for verification.
• Apply RAG and AI agents to verification workflows.
• Develop tools for:
  • Verification planning and testplan analysis
  • Verification tb files generation
  • Coverage gap identification
  • Regression result analysis
  • Debug assistance
  • Verification knowledge search and summarization
• Integrate AI solutions with existing EDA tools and verification environments.
• Collaborate with verification engineers to understand productivity bottlenecks and transform them into AI-assisted workflows.
• Evaluate and benchmark emerging AI technologies for semiconductor engineering applications.
• Present development results and recommendations to project teams.

Qualifications Required

• BS/MS student in Computer Science, Electrical Engineering, Computer Engineering, Artificial Intelligence, or related fields.
• Strong programming skills in Python.
• Familiarity with machine learning, LLMs, or Generative AI technologies.
• Experience with software development and debugging.
• Strong problem-solving and communication skills.
• Ability to work independently and collaboratively in a technical team environment.

• Basic understanding of SoC design and verification, digital IC design, or semiconductor development processes.

What You Will Learn

• Real-world AI applications in semiconductor development.
• Advanced AI Agent and LLM workflows.
• SoC verification methodologies and EDA environments.
• Cross-functional collaboration with semiconductor engineering teams.

Duration

• Approximately 2 quarters (extendable based on project needs).

Working Model

• Onsite preferred (approximately 4 days per week).

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