Pioneer multimodal and video-centric research that moves fast and breaks ground, contributing directly to usable prototypes and scalable systems.
Design and implement novel AI architectures for multimodal language models, integrating text, visual, and audio modalities.
Engineer scalable training and inference pipelines optimized for large-scale multimodal datasets and distributed GPU systems across thousands of GPUs.
Optimize systems and algorithms for efficient data processing, model execution, and pipeline throughput.
Build modular tools for preprocessing, analyzing, and managing multimodal data assets (e.g., images, video, text).
Collaborate cross-functionally with research and engineering teams to translate cutting-edge model innovations into production-grade solutions.
Prototype generative AI applications showcasing new capabilities of multimodal foundation models in real-world products.
Develop benchmarking tools to rigorously evaluate model performance across diverse multimodal tasks.

Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Expertise in Python & Pytorch, including practical experience working with the full development pipeline from data processing & data loading to training, inference, and optimization.
Experience working with large-scale text data, or (bonus) interleaved data spanning audio, video, image, and/or text.
Direct hands-on experience in developing or benchmarking at least one of the following topics: LLMs, Vision Language Models, Audio Language Models, generative video models.
First-author publications at leading AI conferences such as CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS etc.
Nice to have skills: PhD in Computer Vision, Machine Learning, NLP, Computer Science, Applied Statistics, or a closely related field.
Demonstrated expertise in computer vision, video generation foundation model and/or multimodal research.

Senior Research Engineer – Multimodal, Video Foundation Model

Key skills