Applied Machine Learning Researcher

Remote Full-time
This position requires a highly motivated person who wants to help us bridge the gap between research advances and practical applications in generative AI and multimodal foundation models. You will be responsible for evaluating and adapting emerging research, conducting applied research experiments, and working with engineering teams to transform promising approaches into robust solutions, taking into account future hardware design and product needs. In addition, you will have an opportunity to engage and collaborate with several teams across Apple to deliver the best products.
Minimum Qualifications
? Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g. vision, language, video, etc.)
? Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX
? Experience with rapid prototyping, reproduction, and validation of research ideas
? Ability to work in a collaborative environment
? Ability to communicate the results of analyses in a clear and effective manner
? BS and a minimum of 3 years relevant industry experience.

Preferred Qualifications
? Master's or PhD, or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field.
? Track record of translating research into practical applications either through published work or industry experience.
? Deep expertise in multimodal foundation models, with a focus on practical applications.
? Strong applied research experience in at least one major area of model development (data curation, pre-training, fine-tuning, alignment, or evaluation), particularly as it applies to multimodal systems.
? Experience with large-scale training pipelines, including working with large datasets and scaling models across distributed systems.
? Experience bridging research ideas with production constraints.

Core Skills: Machine Learning, Deep Learning, Python, PyTorch, Prototyping
Other Skills: Computer Vision
Seniority: Junior, Mid, Senior

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