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

Apple seeks a Senior ML Research Engineer in Zurich to advance reinforcement learning and agentic reasoning for 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 role focuses on post-training foundation models, specifically using reinforcement learning to improve how AI agents plan, reason, and use tools. The position involves collaborating with researchers in Zurich and core teams in the US to enhance features like Siri for billions of users.

Responsibilities

• Create and expand reinforcement learning techniques to boost reasoning, instruction adherence, and multi-turn dialogue capabilities while minimizing hallucinations in large language models.
• Construct and train AI agents capable of tool usage, planning, and API integration to perform tasks reliably.
• Develop and optimize 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 both 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 United States.

Requirements

• Possess an MSc, PhD, or comparable professional experience in Computer Science, Machine Learning, Electrical Engineering, or a related discipline.
• Demonstrate a robust background in reinforcement learning and deep learning, including practical experience training large-scale models, especially LLMs.
• Show proficiency in Python and contemporary ML frameworks like PyTorch or JAX, with proven skills in distributed training.
• Exhibit strong collaboration skills within interdisciplinary groups and the ability to articulate complex technical ideas to diverse audiences.

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.
• Familiarity with data curation, evaluation frameworks, and safety or guardrail methodologies.
• Capability to design and run large-scale experiments and devise innovative solutions for difficult problems.

About the company

The company is dedicated to contributing to state-of-the-art research in artificial intelligence and machine learning.

• Focus on advancing cutting-edge research in AI.