below marketmost similar roles pay hereabove market
This role pays less than
58%
of similar roles. Most pay
$202,800–$254,812
— the blue band above.
At the midpoint, this role pays about
$214k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
The AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & Distillation joins the Multimodal Intelligence Team to advance foundation models for Apple experiences. You will investigate fundamental questions regarding how multimodal foundation models should be designed, trained, and distilled. Daily responsibilities include developing and evaluating new model architectures, pre-training objectives, data strategies, optimization methods, and teacher–student learning techniques. You will work with PyTorch or JAX and distributed training systems to explore dense, recurrent, state-space, and mixture-of-experts architectures. The role focuses on co-developing frontier models and efficient models for Apple silicon and on-device intelligence. You will solve the technical challenge of transferring capabilities from large foundation models to smaller, efficient models while meeting specific memory, latency, energy, and privacy requirements for on-device deployment.
Develop and evaluate new model architectures, pre-training objectives, and data strategies for multimodal foundation models.
Design teacher-student learning techniques to transfer capabilities from large models to smaller, efficient models.
Conduct large-scale pre-training experiments for multimodal models across language, image, video, and audio modalities.
Incorporate memory, latency, and energy constraints into the research process for on-device deployment on Apple silicon.
Implement architecture and algorithm co-design for memory-efficient and energy-efficient inference.
Execute offline and on-policy distillation using teacher-generated data, logits, and representation signals.
Evaluate pre-trained models for reasoning, instruction following, and multimodal understanding capabilities.
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 model architecture, training objectives, data mixtures, tokenization, 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.
Experience contributing to major foundation-model pre-training efforts or leading architecture experiments that influenced a large training run (preferred).
Research contributions in model architecture, scaling laws, multimodal pre-training, optimization, or related areas (preferred).
Python
PyTorch
LLM Pretraining
Distributed Computing
Spark
Ray
Deep Learning
Synthetic Data Generation
Data Curation
Machine Learning Systems
Data Processing Pipelines
Model Training
Data Filtering
Deduplication
Scaling Laws
Generative AI
Multimodal Foundation Models
Deep Learning
Python
PyTorch
JAX
Computer Vision
Data Pipelines
On-device Machine Learning
Data Curation
Vision-Language Models
Video-Language Models
Python
PyTorch
JAX
Deep Learning
Generative AI
Multimodal Foundation Models
Computer Vision
Data Pipelines
On-device Machine Learning
Data Curation
Vision-Language Models
Video-Language Models