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

Apple· Machine Learning and AI
Apply on company site
Hybrid (Seattle, WA / Sunnyvale, CA)$142k - $263kFull-timePosted 3mo ago

About the role

Research scientist role focused on architectures, pre-training, and distillation for multimodal foundation models with on-device efficiency for Apple products. Involves exploring novel architectures, pre-training objectives, data strategies, optimization, and teacher–student distillation, with impact on Apple silicon, on-device intelligence, and potential publications or open-source artifacts.

Responsibilities

  • 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.

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.
  • 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.

Skills

Required:Large-scale pre-training of language modelsLLM pre-training topicsDeep learning frameworksMultimodal evaluationTransformer architectures
Nice to have:Research communication

Benefits

  • Comprehensive medical and dental coverage
  • Retirement benefits
  • Discounted products and free services
  • Education reimbursement including tuition
  • Eligible for discretionary bonuses or relocation
  • Employee stock programs including stock purchase plan
Last updated Jul 31, 2026. Interested? Apply on the company site.

About Apple

Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

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