Description
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.
Minimum Qualifications
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 Qualifications
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.
