AI Research Scientist, Multimodal Foundation Models

What you'll do

• 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.
• Develop distillation methods using teacher-generated data, logits, representations, and other supervision signals.
• Evaluate pre-trained models across language understanding, reasoning, instruction following, and multimodal capabilities.

What we're looking for

• Master’s degree or equivalent practical experience in machine learning, computer science, or a related technical field.
• Minimum of 6 years of relevant experience.
• Hands-on experience designing, implementing, and running large-scale pre-training experiments for large language models.
• Experience with LLM pre-training topics including 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 (preferred).