As a member of our fast-paced group, you'll have the unique and rewarding opportunity to shape upcoming products from Apple. Our team includes a diversity of backgrounds from applied machine learning engineers with a focus on ML and LLM to experienced distributed systems engineers. As such, we are looking for candidates with applied machine learning experience and strong software engineering skills.
Requirements
PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field
Experience in RAG, LLM Reasoning, and LLM Agent
Experience working in a complex organization with multiple collaborators, strong track record in scaling and launching projects in this setting
MS in Computer Science or related field
5+ years of industry related experience, working in collaborate environments
Utilizing: PyTorch, TensorFlow, and JAX for training and deploying deep learning models
Understanding product requirements and then translating them into modeling tasks and engineering tasks
Building machine-learned models for search relevance ranking, query understanding, and summarization
Pay & Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Additional Information
Do you want to make Siri and Apple products smarter for our users? The Answers, Knowledge & Information team is redefining how hundreds of millions of people use their devices to get information. We are an Applied ML team pushing the limits of apple intelligence, assistant response ranking, and search technologies, while also responsible for a production service. We are part of a wider effort to power information across a variety of Apple products - including Siri, Spotlight, Safari, Messages, Lookup, and more.
In our team, you will be leveraging and improving upon the latest LLM/ML techniques in order to understand queries, rank user intents, rank documents, and generate answers to users' questions. Our team is responsible for training and deploying these models at scale, using the latest advances for online inference optimization.
Apple · Seattle, WA