AI Engineer - Wireless Systems Analysis , Wireless Technologies & Ecosystems

Requirements

• Bachelor's, Master's, or PhD degree in Computer Science, Electrical Engineering, AI, or related field
• Strong experience with LLMs (GPT, LLaMA, Mistral, Claude) and ML frameworks (PyTorch, TensorFlow, Scikit-learn)
• Hands-on expertise in AI/ML or NLP, prompt engineering, embeddings, vector databases, and RAG architectures
• Proficiency in Python for ML model development and log processing

Nice to Haves

• Experience fine-tuning LLMs for specialized domains and knowledge of MLOps practices
• Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex) and vector databases (FAISS, Pinecone, Weaviate)
• Experience building scalable backend systems (REST APIs, microservices) and cloud platforms (AWS, GCP, Azure)
• Solid understanding of 3GPP standards (4G/5G) and wireless communication systems
• Hands-on experience with modem log analysis, protocol stack debugging, or wireless software development
• Experience with cloud-based ML deployment for large-scale log analysis
• Exposure to multimodal AI models and telecom/networking domains
• Strong analytical, problem-solving, and debugging skills with attention to detail
• Excellent communication, presentation, and interpersonal skills
• Proven ability to collaborate effectively and drive multiple projects across diverse teams

What You'll Be Doing

• Designing, developing, and deploying production-grade applications leveraging large language models (LLMs) and generative AI frameworks for wireless systems analysis
• Architecting and optimizing prompt engineering strategies, retrieval-augmented generation (RAG) pipelines, and vector database solutions for wireless log analysis
• Developing cutting-edge ML models utilizing pattern recognition, anomaly detection, deep learning, and reinforcement learning techniques
• Building scalable, reliable backend services, APIs, and microservices
• Integrating AI-powered features into production systems with focus on performance, reliability, and cost efficiency

Perks and Benefits

• Collaboration with world-class hardware and wireless software engineering teams
• Directly impacting experiences for Apple customers globally
• Possibility to work at the intersection of machine learning, natural language processing, and wireless communications
• Ongoing experimentation, feedback loops, and data-driven iteration
• Driving enhancement proposals from concept to commercialization
• Potential to impact iPhone, iPad, and Apple Watch user experiences worldwide