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

Apple•Sunnyvale, CA

About The Position

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. You may thrive in this role if you: Want to invent new foundation-model architectures rather than only adapt existing models. Enjoy combining scientific ambition with real compute, memory, latency, and energy constraints. Believe that small and efficient models can be a frontier research problem, not merely a compression exercise. Are comfortable working across model research, data, systems, and hardware boundaries. Care about translating research into private, useful, and deeply integrated intelligent experiences. Want your work to have both product impact and a presence in the broader research community. Potential research directions include: Novel dense, recurrent, state-space, mixture-of-experts, and hybrid foundation-model architectures. Multimodal pre-training across language, images, video, audio, and sensor-derived representations. Compute-optimal model and data scaling, including data mixtures, curricula, tokenization, and training objectives. Architecture and algorithm co-design for memory-efficient and energy-efficient inference on Apple silicon. Offline and on-policy distillation using teacher-generated data, logits, representations, rationales, and other supervision signals.

Requirements

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.

Nice To Haves

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.

Responsibilities

Investigate fundamental questions about how multimodal foundation models should be designed, trained, and distilled.
Develop and evaluate new model architectures, pre-training objectives, data strategies, optimization methods, and teacher–student learning techniques.
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.
Co-develop frontier models and efficient models for Apple silicon and on-device intelligence.
Design architectures that distill effectively.
Study how teacher and student models should be trained together.
Develop distillation methods that preserve reasoning, multimodal understanding, instruction following, and other important capabilities under constrained model capacity.
Incorporate deployment constraints into the research process—from early architecture experiments and pre-training through distillation and final model evaluation.
Invent new foundation-model architectures rather than only adapt existing models.
Combine scientific ambition with real compute, memory, latency, and energy constraints.
Work across model research, data, systems, and hardware boundaries.
Translate research into private, useful, and deeply integrated intelligent experiences.
Conduct ambitious experiments at scale.
Translate successful ideas into foundation-model technologies that can reach Apple products.
Potentially lead to publications and the open sourcing of selected models, research artifacts, evaluations, or tools.