Join 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.
Description
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 telecom-specific use cases by applying domain knowledge of 3GPP standards (4G/5G) to build intelligent diagnostic systems. You will develop cutting-edge ML models utilizing pattern recognition, anomaly detection, deep learning, and reinforcement learning techniques to identify radio link failures, protocol inefficiencies, and performance bottlenecks while enhancing modem-level diagnostics, network optimization, and performance troubleshooting. Building scalable, reliable backend services, APIs, and microservices, you will integrate AI-powered features into production systems with focus on performance, reliability, and cost efficiency. You will establish robust evaluation, monitoring, and debugging frameworks to ensure model accuracy, performance, and reliability in production environments, while continuously improving systems through experimentation, feedback loops, and data-driven iteration. Collaborating cross-functionally with wireless software engineering, platform architecture, standardization, firmware/protocol development, system test, field test, QA, and carrier engineering teams, you will drive enhancement proposals from concept to commercialization, directly impacting iPhone, iPad, and Apple Watch user experiences worldwide.
Minimum Qualifications
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
Preferred Qualifications
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
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