AI Research Engineer – Pre-training, LLM, Multi-Modal

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Tether.to

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers equipped with multi-nodes & thousands of NVIDIA GPUs. • Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers. • Source, filter, and curate massive-scale textual and multi-modal datasets. • Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance. • Investigate, debug, and eliminate bottlenecks in model efficiency and stability during training runs. • Contribute to the advancement of distributed training systems to ensure scalability and hardware efficiency.

🎯 Requirements

• A degree in Computer Science or related field • Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences) • Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training runs on large, 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 aimed at enhancing intelligence, efficiency and scalability • Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment.

🏖️ Benefits

• Health insurance • Remote work options