AI Research Engineer

Responsibilities

• Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers using 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 to establish robust data pipelines.
• Execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.
• Investigate and resolve bottlenecks in model efficiency, computational performance, and multi-modal alignment stability.
• Contribute to the advancement of distributed training systems to ensure hardware efficiency and scalability.

Requirements

• Degree in Computer Science or a related field.
• Hands-on experience with large-scale LLM or Multi-Modal pre-training runs on distributed GPU clusters.
• Deep knowledge of state-of-the-art transformer and non-transformer architectures.
• Strong expertise in PyTorch and Hugging Face libraries.
• Practical experience in model development, continual pre-training, and deployment.

Preferred Qualifications

• PhD in NLP, Machine Learning, or a related field.
• A solid track record in AI R&D with publications in A* conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL).
• Familiarity with large-scale, distributed training frameworks and tools.

About the Company

Tether is a global leader in digital finance, pioneering solutions that empower businesses to integrate reserve-backed tokens across blockchains. Through our diverse ecosystem—including Tether Finance, Tether Power, Tether Data, and Tether Education—we are driving a financial revolution and pushing the boundaries of technology and human potential.