AI Engineer - Wireless Systems Analysis , Wireless Technologies & Ecosystems
Apple · Munich, Germany · Software and Services
About this role
Apple is hiring a mid-level AI Engineer in the machine learning function based in Munich, Germany. The posting calls out experience with Python, REST APIs, AWS, GCP.
- Role
- AI Engineer
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Location
- Munich, Germany
- Department
- Software and Services
- Posted
- May 12, 2026
More roles at Apple
Job description
from Apple careersJoin Apple's Wireless Technologies and Ecosystems (WTE) organization and be part of a best-in-class engineering team driving innovation in products used by millions worldwide. The Systems Analysis team within WTE is seeking a talented, highly motivated GenAI/LLM engineer to design, develop, and scale advanced AI-driven solutions for wireless systems performance analysis. This role demands deep technical expertise in generative AI, strong software engineering fundamentals, and the ability to translate complex requirements into robust, production-ready systems. Working at the intersection of machine learning, natural language processing, and wireless communications, you will deliver intelligent solutions powered by modern generative AI technologies. You will collaborate with world-class hardware and wireless software engineering teams, directly impacting experiences for apple customers globally. The ideal candidate is a hands-on practitioner with exceptional analytical skills, meticulous attention to detail, and passion for building high-quality, impactful AI solutions. Join Apple to help deliver the next amazing products. In this role, you will design, develop, and deploy production-grade applications leveraging large language models (LLMs) and generative AI frameworks for wireless systems analysis. You will architect and optimize prompt engineering strategies, retrieval-augmented generation (RAG) pipelines, and vector database solutions for wireless log analysis, while fine-tuning, evaluating, and benchmarking LLMs for…