Staff/Senior Machine Learning Engineer, Search & Knowledge Platforms

The AI, Search & Knowledge Platforms team builds amazing products and services for Apples customers while serving as a foundational partner to teams across Apple. The team delivers world-class AI, search, and knowledge systems powering Siri, Apple Intelligence, Safari, and iMessage, and operates the foundational platforms and infrastructure that keep these intelligent experiences running at hyperscale. As part of this group, you will focus on identifying and addressing opportunities to improve the quality and performance of the search and ranking stack that powers world knowledge question answering. This involves developing sophisticated machine learning systems, robust software heuristics, and large language models (LLMs) to understand user queries, retrieve and rank relevant documents, and provide users with direct answers that best satisfy their intent. You will work across the entire stack, applying a broad skill set that ranges from data science and opportunity analysis to ML system design, pipeline development, and quality evaluation. As a member of our fast-paced group, you will have the unique and rewarding opportunity to shape upcoming products from Apple. We are looking for a highly motivated and versatile Machine Learning and Software Engineer with a broad skill set. The ideal candidate combines strong engineering fundamentals with a keen product sense, and is a quick learner who is comfortable jumping into entirely new codebases and architectures. You must be an effective communicator and an adaptable problem solver, willing to take on whatever represents the optimal solution for the product, whether that means training machine learning models, writing robust software heuristics, or iterating on prompt engineering.Identify and address opportunities to improve the quality and performance of the search and ranking stack that powers world knowledge question answering. Understand product requirements and apply a keen product sense to design engineering solutions that directly reflect our product vision. Develop reliable, scalable ways to measure, assess, and compare the impact of different solutions on system quality and performance. Leverage a versatile skill set encompassing data science, opportunity analysis, ML system design, pipeline development, and quality evaluation. Navigate and contribute effectively to entirely new codebases and complex architectures. Communicate findings clearly to cross-functional teams and leadership, backing up recommendations with solid, rigorous data analysis. Learn quickly and adapt to the best technical approach for any given problem, seamlessly shifting between ML model training, software engineering, and prompt engineering.Bachelor's in Computer Science, Artificial Intelligence, Machine Learning, or a related field. 6+ years experience in shipping Search and ML systems Strong programming skills in Python, Go or similar languages, with a solid foundation in software design, data structures, and algorithms. Experience building scalable data pipelines and backend systems, coupled with the ability to quickly navigate and contribute to large, complex, and unfamiliar codebases. Proficiency in data science methodologies, including A/B testing, statistical hypothesis testing and defining offline and online evaluation metrics for ML systems. Experience with prompt engineering Strong product sense with the ability to translate ambiguous product requirements into concrete engineering and modeling tasks. Excellent communication skills, with the ability to back up decisions with rigorous data analysis and present findings clearly to cross-functional partners.Master's/Ph.D in Computer Science, Artificial Intelligence, Machine Learning, or a related field. 4+ years experience in shipping Search and ML systems Hands-on experience in Information Retrieval, Search Ranking, or Natural Language Processing (NLP). Demonstrated experience designing, building, or working with Retrieval-Augmented Generation (RAG) architectures and applications. Familiarity with techniques for optimizing the inference performance, latency, and resource efficiency of LLMs and large machine learning systems in production environments (e.g., model quantization, caching, distributed inference). Experience with training and deploying machine learning models