AI Application Engineer

Advantest Corp

  • Austin, TX
  • 30+ days ago

    Highlights

    As an AI Application Engineer, you will act as the bridge between semiconductor test engineering workflows and AI systems, enabling step-change improvements in productivity such as: test program generation. As a member of the US AI R&D team, you will work closely with 93K R&D engineers, AI engineers, and data scientists to define, develop, and deploy next-generation AI capabilities for the V93000 platform.

    Numbers & Facts

    LocationAustin, TX

    Description

    Job Description

    • This role sits at the intersection of semiconductor test engineering and AI, driving the transformation of traditional test workflows into AI-powered systems.
    • As a member of the US AI R&D team, you will work closely with 93K R&D engineers, AI engineers, and data scientists to define, develop, and deploy next-generation AI capabilities for the V93000 platform.
    • As an AI Application Engineer, you will act as the bridge between semiconductor test engineering workflows and AI systems, enabling step-change improvements in productivity such as:
    • test program generation
    • debug and root cause analysis
    • knowledge-driven engineering workflows
    • You will lead customer engagements for AI solutions, serving as the primary interface for:
    • use case discovery
    • product definition
    • feedback and iterative improvement
    • rollout and adoption of new capabilities
    • You will collaborate with global R&D teams to influence product direction and strategy for AI-enabled test solutions.
    • You will design and execute pre-sales and proof-of-concept activities, including:
    • customer demos
    • benchmark studies
    • pilot deployments
    • You will stay current with advances in AI/ML (e.g., LLMs, RAG, agent workflows) and drive internal and external enablement through workshops and training.

    Technical Environment

    You will work in a hybrid environment combining:

    • Linux-based systems (e.g., Red Hat Enterprise Linux)
    • V93000 / SmarTest development ecosystem
    • Modern AI-assisted development workflows, including:
    • AI-enabled IDEs such as VS-Code, Cursor, GitHub Copilot, and Visual Studio Code
    • Markdown-driven prompt and agent design
    • Python-based automation and AI tooling
    • API-driven systems, version control (Git), and integration with AI platforms and services

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