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AIML - Apple Foundation Models: ML Research Engineer, RL & Agentic Reasoning

Apple· Machine Learning and AI
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Hybrid (Zurich)full-timePosted 2mo ago

About the role

Machine Learning Engineer specializing in foundation model post-training focusing on reinforcement learning for agentic tool use, planning and reasoning to enhance Apple’s foundation models and impact features like Siri. Collaborative role within a Europe-based team of RL/ML experts, with connections to Cupertino and NY foundation model groups.

Responsibilities

  • Develop and scale RL methods to improve reasoning, instruction following, multi-turn dialogue, and reduce hallucinations in large language models.
  • Design and train agents with tool use, planning, and API integration to reliably accomplish tasks.
  • Build and refine reward models, evaluators, datasets, and simulation environments (e.g., for RLHF, RLAIF, and RLVF).
  • Run large-scale experiments, analyze results, and translate findings into both research contributions and practical improvements for Apple Intelligence.
  • Collaborate within a Europe-based team of ~35 RL/ML experts, coordinating closely with Apple’s foundation model groups in the U.S.

Qualifications

  • MSc, PhD, or equivalent research/industry experience in Computer Science, Machine Learning, Electrical Engineering, or a related field.
  • Strong background in reinforcement learning and deep learning, with hands-on experience training large-scale models, particularly LLMs.
  • Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX), with demonstrated experience in distributed training.
  • Ability to collaborate in interdisciplinary teams and clearly communicate complex concepts to both technical and non-technical partners.
  • Publications in top ML/AI venues, or equivalent contributions through open-source or impactful industry work.
  • Hands-on experience with tool use, planning, retrieval, and agentic integrations for LLMs.
  • Experience with data curation, evaluation frameworks, and safety/guardrail methods.
  • Ability to design and implement experiments at scale, and to develop innovative approaches to challenging problems.

Skills

Required:Reinforcement LearningPythonML Frameworks (PyTorch, JAX)Distributed TrainingTool Use, Planning, Retrieval, and Agentic Integrations for LLMs
Last updated Jul 31, 2026. Interested? Apply on the company site.

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