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Internship in Semantic Tool Filtering for Agentic Systems

Internship in Semantic Tool Filtering for Agentic Systems

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companyCARIAD SE
location85049 Ingolstadt, Deutschland
ErschienenErschienen: Heute
Vollzeit

It’s time to reshape automotive mobility for everyone!

At CARIAD, we have the mission to create and deliver leading digital technology for the Volkswagen Group. We’re uniting over 6,000 global experts to build powerful software architectures that enable completely new customer experiences.
Iconic models like the Volkswagen ID. Buzz, Audi Q6 e-tron, and Porsche Macan 4 Electric are already equipped with CARIAD technology.

Join us at CARIAD and become part of an exciting journey to shape the future of mobility!

YOUR TEAM

To support our AI team, we are currently looking for an intern. In your role, you will gain first-hand experience in AI-powered mobility solutions for car interiors and will be an integral part of the team that works on developing innovative AI technologies for the automotive industry.

Our AI team, focused on automotive interiors, is dedicated to creating cutting-edge products that leverage advanced AI technologies to drive our company into the future of mobility.

In this role, you will collaborate with top Generative AI specialists within our organization to create AI-based methodologies aimed at enhancing and innovating the user experience within vehicles. If you are passionate about developing AI technologies that improve in-car user experiences, we invite you to join us in advancing the future of mobility.

WHAT YOU WILL DO

Semantic filtering of tools is essential for intelligently assigning tasks to available tools within an agent system. A language model (LLM) that is aware of the available tools can generate more optimized and manageable task graphs.

Your tasks will be:

  • Research and develop robust semantic filtering techniques to enable efficient tool-supported task graph creation.
  • Evaluate various filtering methods (semantic and non-semantic) to match Python-based tools with user queries or tasks.
  • Apply appropriate filtering methods before task graph generation to ensure that only relevant tools are considered in the initial task decomposition.
  • Refine and assign tools after task graph generation to ensure precise alignment between tasks and the tools available for execution.

    • WHO YOU ARE

    • Enrolled student in the field of Computer Science, Electrical Engineering, or equivalent field
    • Background in Machine Learning, Natural Language Processing and proficiency in Python
    • Foundational understanding of Large Language Models (LLMs) and Agentic systems
    • Fluent in English
    • Team-oriented, growth-minded, goal-focused, and communicative

    NICE TO KNOW

    • Remote work options within Germany
    • Duration: 3 to 6 months
    • 35-hour weeks
    • If you have further questions about the candidate journey at CARIAD, please contact us: careers@cariad.technology