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

Tether Operations Limited

3d • Remote

About The Position

As a member of Tether’s AI model team, the AI Research Engineer will drive innovation in architecture development for models of various scales, including small, large, and multi-modal systems. The role focuses on enhancing model intelligence and efficiency, introducing new capabilities, and advancing the field through research-driven implementation of novel techniques and algorithms.

The engineer will focus on large-scale pre-training for LLMs and multi-modal models; multi-modal data curation and alignment; strengthening model baselines; and identifying and resolving pre-training bottlenecks to improve cross-modal AI performance. Work includes training on distributed, multi-node servers equipped with thousands of NVIDIA GPUs and contributing to scalable distributed training systems and hardware efficiency.

Requirements

• A degree in Computer Science or a related field.
• 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 pre-training, and deployment.

Nice To Haves

• A PhD in NLP, Machine Learning, or a related field.
• A strong track record in AI research and development, including good publications in A conferences.

Responsibilities

• Conduct foundational pre-training for LLMs and multi-modal models integrating text, vision, audio, or other modalities on large distributed servers equipped with multiple 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 large-scale textual and multi-modal datasets, and establish robust data pipelines for efficient pre-training.
• Execute experiments independently and collaboratively, 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.
• Advance distributed training systems to ensure scalability and hardware efficiency on target platforms.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level