AI Research Engineer

Summary

Join Tether's AI model team and drive innovation in architecture development for cutting-edge models. You will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field. Leveraging your deep expertise in LLM architectures and pre-training optimization, you will explore and implement novel techniques and algorithms. Your work will focus on data curation, strengthening baselines, and resolving pre-training bottlenecks to push the limits of AI performance. Tether offers a global, remote work environment and the opportunity to collaborate with bright minds in the fintech space. This role requires a strong background in AI R&D and hands-on experience with large-scale LLM training.

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 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

Responsibilities

• Conduct pre-training AI models on large, distributed servers equipped with thousands of NVIDIA GPUs
• Design, prototype, and scale innovative architectures to enhance model intelligence
• Independently and collaboratively execute experiments, analyze results, and refine methodologies for optimal performance
• Investigate, debug, and improve both model efficiency and computational performance
• Contribute to the advancement of training systems to ensure seamless scalability and efficiency on target platforms