AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & Distillation

What you'll do

• Develop and evaluate new multimodal model architectures, including dense, recurrent, state-space, and mixture-of-experts designs.
• Design and implement pre-training objectives, data mixtures, and training curricula for large-scale foundation models.
• Create teacher-student learning techniques to transfer capabilities from large models to smaller, efficient versions.
• Optimize model architectures specifically for memory, latency, and energy constraints on Apple silicon.
• Conduct large-scale experiments to improve multimodal understanding across text, image, video, audio, and sensor data.
• Develop distillation methods that preserve reasoning and instruction-following capabilities under constrained model capacities.
• Translate research findings into production-ready technologies for integration into the Apple ecosystem.

What we're looking for

• Master's degree or equivalent practical experience in machine learning, computer science, or a related technical field.
• Hands-on experience designing, implementing, and running large-scale pre-training experiments for large language models.
• Experience with LLM pre-training topics including architecture, training objectives, data mixtures, tokenization, scaling, and optimization.
• Proficiency with modern deep learning frameworks such as PyTorch or JAX and distributed training systems.
• Experience evaluating pre-trained models across language understanding, reasoning, instruction following, or multimodal capabilities.
• Strong understanding of transformer-based architectures and current approaches to efficient or scalable foundation-model training.
• Preferred experience contributing to major foundation-model pre-training efforts or leading architecture experiments for large training runs.
• Preferred experience with knowledge distillation techniques and designing teacher-student training pipelines.