RESEARCH INTERN - FINE-TUNING LARGE LANGUAGE MODELS FOR DOWNSTREAM TASKS USING DOMAIN-SPECIFIC KNOWLEDGE (M/W/D)
- München, fortiss GmbH
- Work placement
Who are we?
To strengthen our Machine Learning team, we are looking for a
Research intern / Forschungspraxis
Fine-Tuning Large Language Models for Downstream Tasks Using Domain-Specific Knowledge (M/W/D)
__________
Who We Are and How We Work:
- We work with e-commerce data analysis and prediction.
- We exchange ideas on projects and new tasks in weekly meetings.
- We always keep our common goal in mind and support each other in achieving it.
- Our cooperation is characterized by flat hierarchies and teamwork.
- We always have an open ear for new ideas, and we tackle new challenges together.
- Enthusiasm for scientific work and research projects invites us to exchange ideas.
Your Tasks:
- Develop methods to fine-tune LLMs efficiently using domain-specific knowledge.
- Investigate techniques inspired by LoRA to optimize the adaptation process.
- Design and implement a robust pipeline for LLM operations, including data preparation, training, and evaluation.
Your profile:
- Completion of a bachelor’s degree and current enrollment in a Master’s degree program in electrical engineering, computer science, information systems, or a related field.
- A strong motivation for research, with a passion for learning and sharing knowledge.
- Solid background knowledge in deep learning and natural language processing.
- Experience in object-oriented programming.
- Hands-on experience in implementing deep learning solutions using Python and PyTorch.
- Excellent communication skills in both spoken and written English.
Our offer:
- 9-week Forschungspraxis under the framework of TUM EI.
- An international and dynamic work environment surrounded by highly qualified colleagues.
- Opportunities to gain experience with the latest developments in deep learning and numerous avenues for professional and personal growth.
- Exposure to industry work and research, providing valuable insights into real-world applications.
- The chance to publish your work in academic conferences.
Did we catch your interest?
Job-ID: ML-FP-05-2024
Contact: Tianming Qiu / Stephan Rappensperger
- Ralf Kohlenhuber
- Human Resources Administrator