Internship in Natural Language Processing / Named Entity Recognition at Bosch Group

Ulm, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

31 Dec, 25

Salary

0.0

Posted On

02 Oct, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Natural Language Processing, Named Entity Recognition, Python, Machine Learning, PyTorch, Hugging Face, Transformer Models, Prompt Engineering, Large Language Models, Cloud Platforms, Azure, MLOps, Databricks, Data Science, Artificial Intelligence, Documentation

Industry

Software Development

Description

Company Description At Bosch Rexroth, everything revolves around movement and the success of our customers. With our networked drive and control technologies as well as digital solutions, their machines and systems work efficiently, safely and powerfully. We offer components, system solutions and services for mobile and industrial applications as well as for factory automation. In all areas, we value the know-how and commitment of our employees and enable dynamic careers in an international environment. Around 33,800 people in over 80 countries - and one corporate purpose: "We move industries to make our planet a better place." Do you want to make a difference? Then we look forward to receiving your application to Bosch Rexroth AG. Job Description Become part of our innovative team and contribute your knowledge in Natural Language Processing (NLP). During your internship you will work at the intersection of research and industrial application. You will take over an existing NER (Named Entity Recognition) transformer model for anonymizing English-language corporate data and further develop it through targeted fine-tuning. Furthermore you will independently expand the training datasets to continuously improve model performance and adapt it to new requirements. Also, you will design and implement programmatic prompt engineering to extend our system's functionality to other languages, enabling anonymization across language barriers. Additionally, you will ensure that the adapted models are deployed and operated as a reliable service via automated pipelines in our cloud environment (Azure/Databricks). You will work closely with the team, document your developments and ensure a rapid knowledge transfer within the company. The exact focus of your internship will be flexibly adapted to your experience, interests and skills. Qualifications Education: Studies in the field of Computer Science, Computer Linguistics, Data Science, Artificial Intelligence or comparable with advanced studies Experience and Knowledge: solid knowledge of Python and relevant machine learning libraries (e.g. PyTorch, Hugging Face); practical experience with transformer models for NLP tasks, especially Named Entity Recognition (NER); initial experience in prompt engineering and working with Large Language Models (LLMs); ideally, initial exposure to cloud platforms (preferably Azure) and MLOps tools (e.g. Databricks) Personality and Working Practice: you are characterized by a structured and responsible work ethic Languages: very good in English and German Additional Information Start: according to prior agreement Duration: 6 months Requirement for this internship is the enrollment at university. Please attach your CV, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Oliver Maus (Functional Department) +49 731 49373 445 #LI-DNI You can also find more information about us at www.boschrexroth.de/karriere and on our social media channels, for example Instagram. Move with us. Win together.

Responsibilities

During your internship, you will work on an existing NER transformer model for anonymizing corporate data and further develop it through fine-tuning. You will also expand training datasets and implement prompt engineering for multilingual functionality.