AIML Health Research Scientist: Robust ML for Wellness

Apple Inc.; Zürich; Vor Ort; CHF 140.000 - 210.000; Vollzeit

Zusammenfassung

Apple Inc.'s Personalized AI for Science (PAIS) team in Switzerland seeks a research scientist to design, train, and adapt modern deep learning models on health data, applying rigorous statistical methods to evaluate them across populations and clinical contexts.

You will work with collaborators across research and product teams to translate questions into well-posed problems and publish findings, with emphasis on robustness, uncertainty quantification, and responsible health applications.

Qualifikationen

• Hands-on experience designing and training modern deep learning models, including foundation models and self-supervised pretraining.
• Experience characterizing robustness of ML models under distributional shifts in health data.
• Strong foundation in statistics with ability to draw defensible conclusions from observational data.

Aufgaben

• Designing, training, and adapting modern deep learning models on large-scale physiological and behavioral data.
• Designing rigorous evaluation protocols, including subgroup analyses and robustness to distributional shifts.
• Applying statistical methods to draw defensible conclusions from observational health data.
• Translating clinical and product questions into well-posed problem definitions.
• Engaging with the academic community by publishing papers and presenting your work.
• Collaborating with product teams to transition research into deployed health features.

Kenntnisse

Deep learning

Foundation models

Model robustness

Statistics

Hypothesis testing

Clinical data familiarity

Tools

Python

LLM coding

Jobbeschreibung

Apple Inc.'s Personalized AI for Science (PAIS) team in Switzerland seeks a research scientist to design, train, and adapt modern deep learning models on health data, applying rigorous statistical methods to evaluate them across populations and clinical contexts.

You will work with collaborators across research and product teams to translate questions into well-posed problems and publish findings, with emphasis on robustness, uncertainty quantification, and responsible health applications.