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: 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