AI Intern

AdvantestSan Jose, CA
Onsite

About The Position

Advantest America, a global leader in Semiconductor Test and Measurement, is seeking a motivated and innovative engineering student to explore cutting-edge applications of machine learning and generative AI. This internship provides hands-on experience working with emerging AI systems and integrating them into Advantest’s advanced testing platforms. In this role, you will contribute to research and prototyping efforts focused on LLM-powered reasoning and evaluation systems. You will explore how retrieval-augmented generation (RAG) and agentic workflows can be used to analyze, compare, and assess complex technical content at scale. The internship emphasizes building AI systems that support decision-making, qualitative judgment, and structured feedback in real-world engineering and research environments. You will work with unstructured and semi-structured documents, design multi-step reasoning pipelines, and evaluate system behavior against domain-specific expectations and constraints.

Requirements

  • Currently enrolled in a BS or MS program in Computer Science, Electrical Engineering, or a related field
  • Basic understanding of product management and market analysis methodologies
  • Familiar with key major LLMs, AI tools
  • Strong programming skills in Python
  • Strong written and verbal communication skills, especially for explaining technical decisions
  • Ability to work independently and communicate technical ideas clearly

Nice To Haves

  • Interest in building AI systems that support human judgment and decision-making, not just generation
  • Comfort working with imperfect data, evolving requirements, and subjective evaluation criteria

Responsibilities

  • Develop and execute an approach for a detailed market analysis in different area of the AI tools and companies
  • Competitive analysis of AI usage in the semiconductor market
  • Provide summaries, presentations and guidance about collaborations and partnerships
  • Analyze Project Management execution pipeline and propose efficiency improvements
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