Internship - Performance Engineering and Cost Optimization

Work Environment

• Become part of a technology-driven engineering team within AUDI AG focusing on analysis and optimization of large-scale data processing systems.
• Develop and operate a cloud-based solution processing measurement and trace data at petabyte scale.
• Support data-driven engineering decisions across the company.
• Work in a data-intensive platform where performance, scalability, reliability, and cost efficiency are key.
• Gain practical insights into monitoring, analyzing, and improving modern cloud systems.
• Collaborate closely with software engineers, data engineers, DevOps specialists, and cross-functional teams.
• Contribute to real production environments and understand industrial performance engineering and cost optimization.

Job Purpose/Role

• Analyze and profile a cloud-based system for processing large-scale measurement and trace data.
• Support design and setup of monitoring solutions focusing on CPU usage, memory consumption, and network traffic.
• Evaluate system performance, scalability, and resource efficiency in a production cloud environment.
• Identify performance bottlenecks and cost drivers in collaboration with engineering and DevOps teams.
• Derive, document, and communicate optimization measures for performance and cost efficiency.
• Contribute to cross-functional collaboration with software development and operations teams.

Key Requirements/Skills/Experience

• Enrolled in a degree program in computer science, software engineering, data engineering, or a comparable technical field.
• Very good English skills in written and spoken form.
• Initial experience with Linux-based systems and cloud or server environments.
• Programming experience in Java; knowledge of Scala is an advantage.
• Familiarity with version control systems such as GitHub.
• Basic understanding of CI/CD pipelines, for example GitHub Actions.
• Strong analytical thinking and interest in system performance and efficiency.
• Ability to work independently and approach tasks in a structured and reliable manner.
• Interest in performance engineering, data-intensive systems, or distributed computing.

The final internship period will be agreed upon in consultation with the respective department.