Senior Machine Learning Research Scientist, Generative AI

Senior Machine Learning Research Scientist, Generative AI

Apple

Zurich, Switzerland

Description

As a core member of our AI team, you will lead transformative progress in large-scale language models with a strong focus on reinforcement learning. Your mission is to push the boundaries of planning, reasoning, and agentic intelligence by developing and applying RL techniques to train and refine next-generation foundation models. You’ll work across the entire AI development pipeline—from designing multi-modal architectures to advancing decision-making, evaluation, and large-scale deployment to billions of devices worldwide.

We are seeking a pioneering technical leader with a proven track record in building and scaling language models who is passionate about harnessing reinforcement learning to unlock new levels of reasoning, adaptability, and autonomy in AI.

Minimum Qualifications

• PhD/Master’s degree or equivalent experience in Computer Science, Computer Engineering, or a closely related field.
• Deep expertise in generative AI architectures, with hands-on experience training and scaling large language models (LLMs) and/or multimodal foundation models.
• Industry experience in developing, training, and deploying large-scale ML systems, with emphasis on performance optimization.
• Strong background in deep learning and reinforcement learning, including practical experience applying these methods to LLMs and foundation models.

Preferred Qualifications

• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
• Proven ability to analyze data, diagnose bottlenecks, and optimize large-scale models for performance and efficiency, with creativity in solving complex technical challenges.
• Strong critical thinking, collaboration, and communication skills, with the ability to convey complex concepts to both technical and non-technical stakeholders.
• Demonstrated industry experience delivering ML-driven product features at scale.
• Proven record in designing, training, and optimizing LLMs and/or multimodal foundation models at scale.
• Expert-level understanding of machine learning theory and practice, with specialization in generative modeling and large-scale architectures.
• Research or applied experience in decision-making, reinforcement learning, and agentic reasoning.
• Proficiency in Python and major ML frameworks (PyTorch, TensorFlow, JAX), with advanced skills in debugging, software design, and distributed training.
• Strong research track record, with peer-reviewed publications at leading AI/ML or NLP venues (e.g., NeurIPS, ICML, ICLR, ACL).