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AIML - Apple Foundation Models: Senior ML Research Scientist, RL & Agentic Reasoning
AppleZurich, Zurich, SwitzerlandSenior
Apply Ready to transform how billions of people interact with technology? Apple’s Core Foundation Models team is driving the intelligence that powers experiences across billions of devices worldwide—and we’re looking for exceptional talent to join us! Our Europe-based applied ML team is seeking an experienced ML research scientist to spearhead groundbreaking developments in AI! Collaborate with some of the brightest minds across Apple’s centers in New York, Seattle, and Cupertino as we push beyond conventional limits and redefine the possibilities of artificial intelligence globally. Join us on an inspiring journey where your expertise will be instrumental in shaping the next phase of digital interaction—an extraordinary opportunity awaits.
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.
- 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.
- 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).
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