AIML - Machine Learning Research Engineer, Generative AI

Descrizione del lavoro:

Join Apple's Generative AI team in Zurich as a Machine Learning Engineer specializing in foundation model post-training! Our team advances reinforcement learning (RL) for agentic tool use, planning and reasoning to enhance Apple's foundation models. Our work directly shapes Apple Intelligence features such as Siri-impacting billions of users-while contributing to state-of-the-art research. You'll collaborate with a dedicated group of researchers in Zurich and work closely with Apple's core Foundation Model teams in Cupertino and NY.

Description

In our team, you will: - 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. We value researchers eager to explore the space between fundamental research and applied work-with opportunities to contribute to both scientific progress and real-world applications!

Minimum 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.

Preferred Qualifications

* 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