About Tether
Tether develops digital finance products, including stablecoins, digital asset tokenization services, energy solutions for Bitcoin mining, AI and peer-to-peer technology, secure data-sharing applications, and digital learning services.
Responsibilities
•Conduct foundational pre-training for LLMs and multimodal models using large distributed servers with multiple nodes and thousands of NVIDIA GPUs
•Design, prototype, and scale architectures, tokenizers, and cross-modal alignment layers
•Source, filter, and curate large-scale textual and multimodal datasets, and establish data pipelines for pre-training
•Execute experiments independently and collaboratively, analyze results, and refine training methodologies
•Investigate, debug, and resolve bottlenecks in model efficiency, computational performance, and multimodal alignment stability during long training runs
•Advance distributed training systems to improve scalability and hardware efficiency on target platforms
Requirements
•Degree in Computer Science or a related field
•Hands-on experience contributing to large-scale LLM or multimodal pre-training runs on distributed servers equipped with thousands of NVIDIA GPUs
•Familiarity and practical experience with large-scale distributed training frameworks, libraries, and tools
•Deep knowledge of state-of-the-art transformer and non-transformer modifications for improving intelligence, efficiency, and scalability
•Strong expertise in PyTorch and Hugging Face libraries, with practical experience in model development, continual pre-training, and deployment
•Excellent English communication skills
Nice to have
•PhD in NLP, Machine Learning, or a related field
•A strong AI R&D track record
•Publications at A* conferences
Conditions
No conditions stated in the posting.
