AI Research Engineer (Pre-training - LLM & Multi-Modal)

About Us

At Tether, we're not just building products, we're pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Our team is a global talent powerhouse, working remotely from every corner of the world. We've grown fast, stayed lean, and secured our place as a leader in the industry. If you're passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards.

The Role

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.

You will have a deep expertise in Large Language Model (LLM) and Multi-Modal architectures, a strong grasp of pre-training optimization, and a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements: multi-modal data curation and alignment, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of cross-modal AI performance.

Responsibilities

• Conduct foundational pre-training for LLMs and Multi-Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi-nodes and thousands of NVIDIA GPUs.
• Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers to enhance model intelligence and multi-modal understanding.
• Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines for efficient pre-training.
• Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.
• Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs.
• Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms.

Requirements

• A degree in Computer Science or related field. Ideally a 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, ensuring scalability and impactful advancements in model performance.
• 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.
• Excellent English communication skills.