Internship - Software Engineering - AI-Assisted Baseband Debugging Evaluation, WTE
An Apple internship in Munich with the Wireless Technologies & Ecosystems team: design and build an evaluation framework comparing strategies for using LLMs to accelerate baseband debugging — from LLMs consuming structured debug data to LLMs driving a debugger directly — including tooling, metrics for cost/latency/effectiveness, and documentation of findings.
- Compensation
- Not disclosed
- Location
- Munich
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Basic understanding of AI/LLM concepts (prompting, tool/function calling, agent skills, evaluation metrics)
- Software development experience (e.g. Swift, C/C++)
- Ability to document requirements, experiments, and results clearly
- Proficiency in software engineering tools (e.g. version control like git, Jenkins)
Nice to have
- Knowledge of embedded systems and CPU architectures (e.g. ARM)
- Interest in applying AI to software engineering and developer tooling workflows
- Collaborative, innovative and creative skills
- Fluent English (English-speaking work environment)
Full description
Apple engineers have always focused on one person when they build software — the person who will use it. Working with them, you'll see why every Apple product and service feels intuitive and simple, and why every aspect of this group's work is built around a respect for the customer's needs. The Wireless Technologies & Ecosystems (WTE) team is looking for a highly qualified & self-motivated Intern with a passion for software development and applied AI to help us evaluate how Large Language Models can accelerate baseband debugging. Based in Germany, we are passionate about wireless technology and work closely with developers across Apple, worldwide. Speaking German is not required for this role.
Innovation driven work environment which provides opportunities to innovate, to share and develop ideas in an open forum. In this internship you will design and build an evaluation framework that compares different strategies for applying Large Language Models to baseband debugging — from LLMs consuming pre-analyzed, structured debug data, to LLMs interacting directly with a debugger on raw coredumps and traces. You will develop the tooling needed to run these approaches end-to-end, define the metrics to compare them across cost, latency, and effectiveness, and document your findings and recommendations. We would be excited to have you join our team and work on products that millions of customers use daily! If this is you, we'd love to hear from you.
Minimum Qualifications
- Basic understanding of AI/LLM concepts (prompting, tool/function calling, agent skills, evaluation metrics)
- Software Development (e.g. Swift, C/C++)
- Knowledge of embedded systems and CPU architectures (e.g. ARM) is a plus
- Ability to document requirements, experiments, and results clearly
- Proficiency in software engineering tools (e.g. SW version control systems like git, Jenkins, etc)
Preferred Qualifications
- Interest in applying AI to software engineering and developer tooling workflows
- Collaborative, innovative and creative skills
- Fluent English (English speaking work environment)