Apple - Seattle, WA
posted 4 months ago
The AIML Information Intelligence team at Apple is at the forefront of creating groundbreaking technology in artificial intelligence, machine learning, and natural language processing. This team is responsible for developing features that redefine how hundreds of millions of users interact with their devices, including Siri, Spotlight, Safari, Messages, and Lookup. The team is also focused on state-of-the-art generative AI technologies based on Large Language Models (LLMs) to enhance Apple's devices and services in the cloud. As a Senior Machine Learning Engineer/Scientist, you will engage in large-scale machine learning and deep learning projects aimed at improving Query Understanding and Ranking of search results. This role involves developing sophisticated machine learning models, utilizing word embeddings and deep learning techniques to assess match quality, and employing online learning to adapt to changes in user behavior. You will leverage vast amounts of data and signals from millions of users to combine information from multiple sources, ensuring that users receive results that best satisfy their intent and information-seeking needs. In this position, you will collaborate with researchers and data scientists to develop, fine-tune, and evaluate domain-specific Large Language Models for various applications within Apple's AI-powered products. You will also conduct applied research to transition cutting-edge generative AI research into production-ready technologies. The role requires a strong understanding of product requirements, which you will translate into modeling and engineering tasks. You will be responsible for building machine learning models for tasks such as entity linking, intent classification, entity recognition, search relevance, ranking, and query understanding. Additionally, you will integrate search functionalities into Apple products and build end-to-end production systems that include query understanding and ranking capabilities, utilizing technologies such as Spark, Hadoop MapReduce, Hive, and Impala for distributed data processing.