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Apple Machine Learning Engineer Data & Innovation China, Beijing, Beijing 208956280
Today
Description
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 tackle complex data problems. The solutions you develop will significantly impact future Apple software and hardware products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to designing and developing a comprehensive data generation and curation framework for foundation models at Apple. You will also be responsible for creating robust model evaluation pipelines, integral to the continuous improvement and assessment of foundation models. Additionally, your role will entail an in-depth analysis of multi-modal data to understand its influence on model performance. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.
Minimum Qualifications
- Deep technical skills in one or more machine learning areas, such as computer vision, 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.
- 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.
- Hands-on mentality to own engineering projects from inception to shipping products and the ability to work independently and as part of a cross-functional team.
- 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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