AI Intern

Description
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.

Location: Austin, TX or San Jose, CA (headquarters)

Role Overview
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.

Key Responsibilities

• Design and implement multi-step agentic workflows for analyzing and evaluating technical content.
• Develop RAG-based pipelines that combine internal documentation and reference materials with LLM reasoning.
• Build AI agents capable of:
  • Comparing proposed ideas or approaches against known solutions or baselines
  • Identifying conflicts, gaps, redundancies, or lack of novelty
  • Producing structured assessments and constructive feedback
• Experiment with prompting strategies, planning, reflection, and tool usage to improve reasoning quality and consistency.
• Evaluate and iterate on system performance using qualitative and semi-quantitative metrics.
• Collaborate with engineers and researchers to translate ambiguous evaluation criteria into actionable AI workflows.

Requirements:

• Currently enrolled in a BS or MS program in Computer Science, Electrical Engineering, or a related field
• Strong programming skills in Python
• Hands-on experience with LLMs, including prompt design and experimentation
• Familiarity with retrieval-augmented generation (RAG) concepts (e.g., embeddings, vector search, context assembly)
• Experience or coursework involving multi-step workflows, pipelines, or agent-based systems
• Strong written and verbal communication skills, especially for explaining technical decisions
• Ability to work independently and communicate technical ideas clearly

Additional Skills Preferred (but not required):

• Experience with agent orchestration frameworks (e.g.,LangGraph, LangChain, custom agent loops)
• Exposure to LLM evaluation techniques, including rubric-based scoring, pairwise comparison, or ranking tasks
• Experience working with document-heavy or text-analysis problems (e.g., reviews, reports, proposals, research papers)
• Familiarity with semantic similarity, novelty detection, or content comparison techniques
• 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