Staff Machine Learning Engineer, Information Intelligence

Apple•Seattle, WA

11h

About The Position

Apple is where individual imaginations come together, committing to the values that lead to great work. The Information Intelligence team is redefining how billions of people use their devices to get information. This Applied ML team pushes the limits of question answering, assistant response ranking, and search technologies, while also being responsible for a production service. The team is part of a wider effort to power information across various Apple products, including Siri, Spotlight, Safari, Messages, and Lookup. As a member of this team, you will leverage and improve upon the latest deep learning techniques, such as LLM and RAG, to understand queries and user intents, rank documents, and find useful answers. The team is responsible for training, fine-tuning, and deploying these models at scale, utilizing the latest advances for online inference optimization. As a Staff Machine Learning Engineer, you will play a critical role in developing world-class Search and Q&A experiences for Apple customers with cutting-edge search technologies and large language models. This fast-paced group offers the unique opportunity to shape upcoming Apple products. The team comprises individuals with diverse backgrounds, from applied scientists focused on NLP to experienced distributed systems engineers. Candidates should possess an in-depth understanding of machine learning fundamentals, applied machine learning experience, and strong software engineering skills. The team is tasked with delivering next-generation Search and Question Answering systems across Apple products.

Requirements

MS in Computer Science or related field
10+ years of work experience in machine learning, deep learning or related field
10+ years experience in shipping Search and Q&A technologies and ML systems
Excellent programming skills in mainstream programming languages such as C++, Python, Scala, and Go
Experience delivering tooling and frameworks to evaluate individual components and end-to-end quality
Strong analytical skills to systematically identify opportunities to improve search relevance and answer accuracy
Strong written and verbal communication with the ability to articulate complex topics
Excellent interpersonal skills and teamwork; demonstrated ability to connect and collaborate with others
Passion for building phenomenal products and curiosity to learn
In-depth understanding of machine learning fundamentals
Applied machine learning experience
Strong software engineering skills

Nice To Haves

PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field
Strong industry background and experience in search and related technologies (LLMs, Machine Learning, NLP, Information Retrieval, Question Answering)
Strong and validated experience of ML development and production systems
Experience working with foundation models and LLMs

Responsibilities

Shape upcoming products from Apple
Deliver next-generation Search and Question Answering systems across Apple products including Siri, Safari, Spotlight, and more
Shape how people get information by leveraging Search and applied machine learning expertise along with robust software engineering skills
Collaborate with Search and AI engineers on large scale machine learning to improve Query Understanding, Retrieval, and Ranking
Develop fundamental building blocks needed for AI powered experiences such as fine-tuning and reinforcement learning
Push the boundaries on document retrieval and ranking
Develop sophisticated machine learning models
Use embeddings and deep learning to understand the quality of matches
Implement online learning to react quickly to change and natural language processing to understand queries
Work with petabytes of data and combine information from multiple structured and unstructured sources to provide the best results and accurate answers to satisfy users' information-seeking needs
Understand product requirements then translate them into modeling and engineering tasks
Analyze search ranking and relevance requirements, issues, and opportunities