About the job
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.
Responsibilities
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.
Qualifications
Minimum
Deep technical skills in one or more machine learning areas, such as computer vision, audio, combinatorial optimization, causality analysis, natural language processing, and deep learning.
Strong software development skills with proficiency in Python; hands-on experience working with deep learning toolkits like PyTorch, TensorFlow, or JAX (one of).
5+ years of experience developing and evaluating ML applications, demonstrating a passion for understanding and improving model/data quality.
Preferred
Deep understanding of multi-modal foundation models.
Staying up-to-date with emerging trends in generative AI and multi-modal LLMs.
The ability to formulate machine learning problems, design, experiment, implement, and communicate solutions effectively with multi-functional teams.
Demonstrated publication records in relevant conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc.).
Track records of adopting ML to solve cross-disciplinary problems.