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Machine Learning Research Scientist - Health AIML

Apple
Posted a month ago, valid for 17 days
Location

Seattle, WA 98164, US

Salary

Competitive

Contract type

Full Time

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Sonic Summary

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  • The Health AIML team at Apple is seeking a Machine Learning Research Scientist with a PhD in a relevant field and industry work experience.
  • The role focuses on developing next-generation multimodal models to enhance health and fitness technologies for millions of users.
  • Candidates should have a proven track record in contributing to major LLM training runs and publishing state-of-the-art research.
  • Strong expertise in deep learning frameworks like PyTorch, JAX, or TensorFlow is required, along with experience in training multimodal models.
  • The position offers a competitive salary, which is commensurate with experience, and candidates should have a minimum of 3 years of relevant industry experience.
The Health AIML team is at the forefront of machine learning and health science at Apple. We are a close-knit team of research scientists, software engineers and machine learning engineers passionate about delivering innovative technologies that impact millions of users. We are looking for a Machine Learning Research Scientist with strong dedication to solving real-world problems in health and fitness that enrich our customers' lives.

Description


We’re developing next-generation multimodal models to create intelligent health and fitness experiences. This role requires someone with strong expertise in large multimodal models to work at the intersection of AI and health to build foundational models that scale to billions of users worldwide. Your work will shape the future of health and fitness technologies at Apple. We are looking for a research lead to guide multimodality research. You will lead the development of foundational technology that enables models to understand health and fitness data.

Minimum Qualifications


PhD in Computer Science/Engineering, Machine Learning, Statistics, Mathematics or related field. Industry work experience. Experience landing contributions to major LLM training runs. Proven track record of publishing SOTA. Strong skills with deep learning frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications


Experience in training and evaluating multimodal models. Understand of time-series modeling, self-supervised learning, and cross-modal training. Ability to thoroughly evaluate and improve deep learning architectures in a self-directed fashion. Motivated by safely deploying LLMs in the health and fitness space.



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