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

Apple seeks an ML Research Engineer in Zurich to advance reinforcement learning for agentic reasoning within its foundation models, directly influencing Apple Intelligence features.

The company is looking for a Machine Learning Engineer to join its Generative AI team in Zurich. This position focuses on post-training foundation models, specifically applying reinforcement learning to enhance tool use, planning, and reasoning capabilities. The role has a direct impact on Apple Intelligence features like Siri, serving billions of users worldwide.

Responsibilities

• Create and expand reinforcement learning techniques to boost reasoning, instruction adherence, and multi-turn dialogue while minimizing hallucinations in large language models.
• Construct and train agents capable of using tools, planning, and integrating APIs to perform tasks reliably.
• Develop and improve reward models, evaluation metrics, datasets, and simulation environments for methods such as RLHF, RLAIF, and RLVF.
• Execute large-scale experiments, interpret results, and convert insights into research outputs and practical enhancements for Apple Intelligence.
• Work within a European team of approximately 35 RL and ML specialists, maintaining close coordination with foundation model groups in the U.S.

Requirements

• Hold an MSc, PhD, or possess equivalent research or industry experience in Computer Science, Machine Learning, Electrical Engineering, or a related discipline.
• Demonstrate a strong foundation in reinforcement learning and deep learning, with practical experience training large-scale models, especially LLMs.
• Be proficient in Python and modern ML frameworks like PyTorch or JAX, with proven experience in distributed training.
• Collaborate effectively within interdisciplinary teams and communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Nice to have

• Publications in leading ML/AI conferences or equivalent contributions via open-source projects or significant industry work.
• Practical experience with tool use, planning, retrieval mechanisms, and agentic integrations for LLMs.
• Background in data curation, evaluation frameworks, and safety or guardrail methodologies.
• Capability to design and run large-scale experiments and develop innovative solutions for complex problems.

About the company

The organization values researchers who are keen to explore the intersection of fundamental research and applied work. Employees have the opportunity to contribute to both scientific progress and real-world applications.

• A focus on bridging the gap between fundamental research and practical application.
• Opportunities to drive scientific advancement while delivering tangible real-world solutions.