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AI Intern

Job in Austin, Travis County, Texas, 78719, USA
Listing for: Advantest America Corporation
Apprenticeship/Internship position
Listed on 2026-06-02
Job specializations:
  • Software Development
    AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
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

Role Overview

In this role, you will contribute to research initiatives focused on generative and agentic AI systems, evaluating how these technologies can improve user experience and operational efficiency across Advantest platforms. You will work with structured workflows and multi-step AI systems to explore practical applications in engineering environments.

Key Responsibilities

* Experiment with LLM-based agent configurations on internal Advantest infrastructure

* Evaluate model performance using domain-specific documentation as contextual input for a variety of queries and tasks

* Design and analyze agentic workflows, including reflection, tool usage, and planning strategies

* Develop and iterate on multi-step AI pipelines for task execution and reasoning

Requirements:

* Currently enrolled in BS/MS Computer Science, EE or related program.

* Strong programming skills in Python

* Basic understanding of machine learning or large language models

* Ability to work independently and communicate technical ideas clearly

Additional Skills Preferred (but not required):

* Familiarity with agentic systems or orchestration frameworks (e.g., Lang Graph)

* Experience working with multi-step workflows, pipelines, or backend systems

* Exposure to LLM prompting, evaluation, or experimentation

* Interest in applying AI systems to real-world engineering problem
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