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AIML - Foundation Model Post-Training Research Scientist
AppleZurich, Zurich, Switzerland
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! Join our Europe-based applied ML team as we build the next generation of Apple's foundation models, pushing the boundaries of agentic capabilities to enable new products and experiences for our customers.
As a senior member of the team, you will work closely with researchers and systems engineers to advance our foundation models on capabilities critical to Apple Intelligence products. You'll collaborate with teams across Apple's engineering hubs—including New York, Seattle, and Cupertino—to push the frontier of agentic capabilities such as coding, software engineering, device use, ui understanding and navigation, agentic search, and more. If you thrive at the intersection of reinforcement learning, reasoning, and agentic AI — and love turning research into products used by billions — this is the role for you.
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 multi-modal foundation models.
- Industry experience in developing, training, and deploying large-scale ML systems, with emphasis on model performance optimization.
- Demonstrated industry experience on developing agentic capabilities for modern foundation models.
- Proficiency in Python and modern ML frameworks (PyTorch/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.
- Background in deep learning and reinforcement learning, including practical experience applying these methods to LLMs and foundation models.
- Demonstrated industry experience delivering ML-driven product features at scale.
- Proven record in designing, training, and optimizing LLMs and/or multi-modal 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.
- Strong research track record, with peer-reviewed publications at leading AI/ML or NLP venues (e.g., NeurIPS, ICML, ICLR, ACL).