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

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

The Multimodal Intelligence Team is building the next generation of foundation models for Apple experiences. We are looking for a research scientist to advance the architectures, pre-training methods, and distillation techniques that make highly capable multimodal models practical across the Apple ecosystem. Our research spans the full foundation-model lifecycle: model architecture, pre-training objectives, data mixtures, optimization, scaling, distillation, and evaluation. A defining challenge of our work is to develop models that combine broad intelligence with the memory, latency, energy, and privacy requirements of on-device deployment.

You will have the opportunity to shape new research directions, conduct ambitious experiments at scale, and translate successful ideas into foundation-model technologies that can reach Apple products. Where appropriate, this work may also lead to publications and the open sourcing of selected models, research artifacts, evaluations, or tools.

In this role, you will investigate fundamental questions about how multimodal foundation models should be designed, trained, and distilled. You will develop and evaluate new model architectures, pre-training objectives, data strategies, optimization methods, and teacher–student learning techniques. Your work will explore how capabilities developed in large foundation models can be effectively transferred to smaller, more efficient models without treating distillation as an isolated downstream step.

A major focus of the role will be the co-development of frontier models and efficient models for Apple silicon and on-device intelligence. This includes designing architectures that distill effectively, studying how teacher and student models should be trained together, and developing distillation methods that preserve reasoning, multimodal understanding, instruction following, and other important capabilities under constrained model capacity. Rather than treating deployment constraints as an afterthought, you will incorporate them into the research process—from early architecture experiments and pre-training through distillation and final model evaluation.

Minimum Qualifications:

• Hands-on experience designing, implementing, and running large-scale pre-training experiments for large language models.
• Experience with LLM pre-training topics such as model architecture, training objectives, data mixtures, tokenization, curricula, scaling, and optimization.
• Strong 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.
• Master's degree, or equivalent practical experience in machine learning, computer science, or a related technical field.

Preferred Qualifications:

• Experience contributing to major foundation-model pre-training efforts or leading architecture experiments that influenced a large training run.
• Research contributions in model architecture, scaling laws, multimodal pre-training, optimization, efficient attention, mixture-of-experts, state-space models, or related areas.
• Experience with knowledge distillation, including offline or off-policy distillation, on-policy distillation, self-distillation, sequence-level distillation, logic matching, or representation transfer.
• Experience designing teacher–student training pipelines or transferring capabilities from large foundation models to smaller models.
• Experience with multimodal models spanning language, vision, video, audio, or other sensor modalities.
• Understanding of inference efficiency, memory hierarchy, hardware accelerators, or hardware–software co-design.
• Strong publication record, influential open-source contributions, or an equivalent record of applied research impact

Pay & Benefits:

At Apple, base pay is one part of our total compensation package and is determined within a…