Member of Technical Staff - Multimodal Tokenization

xAI•Palo Alto, CA

40d•Onsite

About The Position

About xAI xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. About xAI xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All engineers and researchers are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. Focus Training multimodal tokenizers at the web scale for unified interface with multimodal language models. Pushing boundaries in spatial-temporal compression, world modeling, multimodal reasoning, cross-modal alignment, and emergent capabilities. Rapidly implementing the latest state-of-the-art methods from the deep learning literature. Innovating new ideas for pretraining and new scaling paradigm.

Requirements

Strong engineering skills with passion on model-hardware co-design
Expert in ML and large model scaling, familiar with all kinds of scaling laws.
Familiar with distributed training, multi-GPU neural network training and experience on optimizing ML training efficiency.
Familiar with state-of-the-art techniques for multimodal tokenizers, especially image, video and audio.