A
Machine Learning Research Scientist - Health AIML
AppleSeattle, Washington, United States$171,600 - $258,100
Apply 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.
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 senior researcher to guide multimodality research. You will focus on the development of foundational technology that enables models to understand health and fitness data.
- Lead research into health and fitness representation models and multimodal models.
- Design, prototype and scale up new architectures to improve model intelligence.
- Execute and analyze experiments autonomously and collaboratively.
- Study, debug, and optimize model performance and computational performance.
- Contribute to training and inference infrastructure.
- Guide technical and architectural decision.
- 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.
- 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.