About The Position

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.

Requirements

  • 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.

Nice To Haves

  • 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.
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