Job Description

We are looking for highly motivated students (spring/summer/fall 2026) to join our research team in developing new deep learning/AI algorithms for various real-world applications of AI, including but not limited to:

• large language model, vision language model,
• AI efficiency, and
• security of agentic AI with emphasis on formal modeling and formal verification approaches

This internship role will focus on the following responsibilities:

• Design, development and implementation of novel deep learning algorithms targeted towards scalable and verifiable data-curation and auto-labeling on public benchmarks and multi-modal Bosch data from application areas such as automated driving and robotics
• Technical discussions with the research team towards creation of new ideas and improve upon the existing solutions.
• Clean, efficient, and well-documented implementation of new AI methods.
• Code contributions to our deep learning framework.
• Publish at leading AI conferences, and ultimately shape the future of Bosch products and services.

Qualifications

Required Qualification:

• Pursuing MS or PhD degree in Computer Science / Engineering, Data Science, Electrical Engineering, or Mathematics.
• Strong foundation and broad knowledge of Machine Learning algorithms and principles.
• Programming experience in Python + PyTorch.
• Excellent communication skills.

Desired Qualification:

• Experience with working on Machine Learning, or Deep Learning projects.
• Established publication record in Machine Learning or Computer Vision or Natural Language Processing or related fields.
• Good coding practices, ability to write efficient and readable code.
• Good writing and presentation skills.

Additional Information

** Equal Opportunity Employer, including disability / veterans. __**

*Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.