苹果AIML - Machine Learning Engineer, Data & Machine Learning Innovation

The objective of this role is to elevate Apple’s voice assistant and search to a new level of intelligence and accuracy through the application of advanced techniques. This encompasses various areas such as enhancing data management, optimizing pipeline processes, improving services, and refining modeling algorithms. Additionally, the team collaborates closely with other engineers to swiftly develop experiments and implement prototypes. By consistently tackling novel challenges, they strive to create a remarkable product that prioritizes accuracy, usability, and optimal performance. This unique opportunity at the forefront of machine learning and software engineering combines a diverse set of skills and innovation. Your work will have a profound impact on hundreds of millions of users worldwide.

Design and build infrastructures to support features that empowers billions of Siri, Spotlight, and Safari users. Perform language processing, statistical analysis, and user intent analysis to support your hypothesis for how to improve product-outcomes. Leverage proprietary parallel data processing platform to process web scale data to deliver product features and improvements. Design & run/deploy various metrics and evaluations of features/improvements using a variety of tools like grading, logs processing, pre-launch and holdback A/Bs. Present results of analysis to team and leadership across Apple.

The objective of this role is to elevate Apple’s voice assistant and search to a new level of intelligence and accuracy through advanced machine learning techniques. We are looking for someone with a strong passion for AI-driven applications. In this role, you will develop a deep understanding of user use cases and create high-quality evaluation datasets. You will leverage large language models and search tool integrations to answer user questions across diverse everyday scenarios. A core responsibility will be conducting systematic failure analysis to continuously improve accuracy and user experience. On a day-to-day basis, your work will span model training, tool development, system integration, performance testing, and functional test design.

As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying innovative research in foundation models to with a particular focus on audio data. This includes working across the full ML pipeline—from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning with task-specific datasets. The solutions you develop will have a significant impact on future Apple software and hardware products, as well as the broader ML ecosystem. Your responsibilities will extend to designing and developing a comprehensive multi-modal data generation and curation framework for foundation models at Apple. You will also contribute to building robust model evaluation pipelines that support continuous improvement and performance assessment. In addition, the role involves analyzing multi-modal data to better understand its influence on model behavior and outcomes. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. YOUR WORK MAY SPAN VARIOUS APPLICATIONS, INCLUDING: Designing self-supervised and semi-supervised representation learning pipelines, and fine-tuning strategies for tasks like speech recognition and speaker identification. Applying data selection techniques such as novelty detection and active learning across multi modalities to improve data efficiency and reduce distributional gaps. Modeling data distributions using ML/statistical methods to uncover patterns, reduce redundancy, and handle out-of-distribution challenges. Rapidly learning new methods and domains as needed, and guiding product teams in selecting effective ML solutions.