As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art 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.

Enhancing current products and future hardware platforms with multi-modal perception data, particularly through audio and sensor fusion techniques.

Designing self-supervised and semi-supervised representation learning pipelines, along with audio-specific pre-training and fine-tuning strategies for tasks like speech recognition and speaker identification.

Applying data selection techniques such as novelty detection and active learning across modalities—audio, vision, language, and 3D—to improve data efficiency and reduce distributional gaps.

Modeling data distributions using modern 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.