Finde einen Job 
Finde eine Ausbildung 
Top Arbeitgeber 
Top Universitäten 
Für Unternehmen Auf einen Blick
- Aufgaben: Research and develop AI methodologies to enhance in-car user experiences.
- Arbeitgeber: CARIAD is transforming automotive mobility with cutting-edge digital technology for the Volkswagen Group.
- Mitarbeitervorteile: Enjoy remote work options, flexible hours, and a collaborative team environment.
- Warum dieser Job: Join us to innovate mobility solutions and shape the future of in-car experiences!
- Gewünschte Qualifikationen: Must be an enrolled student in Computer Science or related field with Python skills.
- Andere Informationen: Duration: 3 to 6 months; 35-hour weeks. Contact us for more info!
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
We offer you an exciting opportunity for your master thesis in our AI team in the field of automotive interiors. For the following topic you get the responsibility: Collaborate with Generative AI specialists to assist in the development of AI-powered mobility solutions. The goal of this master thesis is 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
- Research and development for fine-tuning of open-source small language models (SLMs)
- Methodologies and building pipelines for generating high-quality synthetic Data for training SLMs
- Establishing an evaluation framework to assess the performance of finetuned SLMs.
WHO YOU ARE
- Enrolled student in the field of Computer Science, Electrical Engineering, or equivalent field (please specify the expected date of your graduation or end of enrollment).
- Background in machine learning and proficiency in Python
- Foundational understanding of large language models (LLMs)
- Team-oriented, growth-minded, goal-focused, and communicative
- Fluent in English
NICE TO KNOW
- Remote work options within Germany
- Duration: 3 to 6 month; 35-hour weeks
- If you have further questions about the candidate journey at CARIAD, please contuct us: careers@cariad.technology
Master Thesis - Training Small Language Models, Synthesis of Training Data, and Evaluation Strategy (m/f/d) Arbeitgeber: CARIAD SE
Kontaktperson:
CARIAD SE HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: Master Thesis - Training Small Language Models, Synthesis of Training Data, and Evaluation Strategy (m/f/d)
✨Tip Number 1
Make sure to familiarize yourself with the latest advancements in small language models and generative AI. This knowledge will not only help you during the interview but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with online communities or forums related to machine learning and AI. Networking with professionals in these spaces can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss specific projects or experiences where you've applied machine learning techniques, especially in Python. Being able to articulate your hands-on experience will set you apart from other candidates.
✨Tip Number 4
Research CARIAD's current projects and technologies, particularly those related to automotive interiors. Showing that you understand their mission and how your skills align with their goals can make a strong impression.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: Master Thesis - Training Small Language Models, Synthesis of Training Data, and Evaluation Strategy (m/f/d)
Tipps für deine Bewerbung 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the responsibilities and requirements for the Master Thesis position. Highlight your relevant skills and experiences that align with the role.
Tailor Your CV: Customize your CV to emphasize your background in computer science, electrical engineering, or related fields. Include specific projects or coursework related to machine learning and Python programming.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI technologies and how they can enhance user experiences in vehicles. Mention any relevant experience you have with small language models or synthetic data generation.
Highlight Teamwork and Communication Skills: Since the role emphasizes being team-oriented and communicative, provide examples in your application that demonstrate your ability to work collaboratively and effectively communicate within a team setting.
Wie du dich auf ein Vorstellungsgespräch bei CARIAD SE vorbereitest
✨Show Your Passion for AI
Make sure to express your enthusiasm for developing AI technologies, especially in the context of enhancing user experiences in vehicles. Share any relevant projects or experiences that highlight your interest in this field.
✨Demonstrate Technical Knowledge
Be prepared to discuss your background in machine learning and Python. Highlight any specific projects where you've fine-tuned models or worked with synthetic data generation, as this aligns closely with the responsibilities of the role.
✨Communicate Your Team Spirit
CARIAD values team-oriented individuals. Be ready to share examples of how you've successfully collaborated with others in past projects. Emphasize your growth mindset and how you contribute to a positive team environment.
✨Ask Insightful Questions
Prepare thoughtful questions about the AI team's current projects or the methodologies they use. This shows your genuine interest in the role and helps you understand how you can contribute effectively.
