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Seattle, Washington, United States Machine Learning and AI

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.

• 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.

• 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.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides an opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

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