ML Research Internship: Foundation Models & RL Agents

Apple Inc.

Zürich

Vor Ort

CHF 20.000 - 27.000

Teilzeit

Vor 4 Tagen
Sei unter den ersten Bewerbenden
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Zusammenfassung

Apple Inc. in Zurich is seeking an ML research intern to contribute to foundation model development and agentic capabilities, with emphasis on Reinforcement Learning.

You’ll work with an applied ML team of ~35 engineers in Zurich and collaborate with experts in the US. During the internship you will conduct research and development for Apple foundation models powering AI experiences across platforms, translating research into scalable real-world performance, and exploring alignment and safety

Qualifikationen

  • PhD student in ML or related fields.
  • Strong background in RL and DL with hands-on experience training large models, in particular LLMs.
  • Experience with training methodologies including pre-training, fine-tuning, and alignment approaches.
  • Strong programming skills in Python, PyTorch, and JAX.

Aufgaben

  • Research and develop novel approaches for enhancing foundation model capabilities.
  • Leverage state-of-the-art RL methods to improve reasoning and planning in agents.
  • Design and train agents with tool calling, planning, and API integration.
  • Investigate multimodal foundation models spanning text, vision, audio, and other modalities.
  • Collaborate on model alignment, safety, and responsible AI practices.

Kenntnisse

Reinforcement Learning
Deep Learning
Python programming

Ausbildung

PhD student in Machine Learning

Tools

Python
PyTorch
JAX

Jobbeschreibung

Apple Inc. in Zurich is seeking an ML research intern to contribute to foundation model development and agentic capabilities, with emphasis on Reinforcement Learning.

You’ll work with an applied ML team of ~35 engineers in Zurich and collaborate with experts in the US. During the internship you will conduct research and development for Apple foundation models powering AI experiences across platforms, translating research into scalable real-world performance, and exploring alignment and safety

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