Join Tether and shape the future of digital finance. As a member of the AI model team, you will drive innovation in architecture development for cutting-edge models of various scales, including small, large, and multi-modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field.
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You will have deep expertise in video generation model architectures with a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements in data curation, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of model performance.
Responsibilities:
- Pioneer multimodal and video-centric research that contributes 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.
Qualifications:
- 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 interleaved data spanning audio, video, image, and/or text.
- Direct hands-on experience in developing or benchmarking topics such as LLMs, Vision Language Models, Audio Language Models, or generative video models.
Nice to Have Skills:
- PhD in Computer Vision, Machine Learning, NLP, Computer Science, Applied Statistics, or closely related field.
- Demonstrated expertise in computer vision, video generation foundation models, and/or multimodal research.
- First-author publications at leading AI conferences such as CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, etc.
Language Requirements:
- Excellent English communication skills are required.
