AI Research Engineer (Pre-training - LLM & Multi-Modal)
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Key skills for this role
About the Role
Tether is hiring an AI Research Engineer to drive innovation in pre-training LLM and multi-modal models. You will design architectures, curate datasets, and optimize large-scale distributed training on thousands of GPUs.
Key Skills for This Role
Responsibilities
- Conduct foundational pre training for LLMs and Multi Modal models on large distributed servers with 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 for efficient pre training
- Independently and collaboratively execute experiments, analyze results, and refine training methodologies
- Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi modal alignment stability
- Contribute to the advancement of distributed training systems for seamless scalability and hardware efficiency
Requirements
- Degree in Computer Science or related field; ideally PhD in NLP, Machine Learning, or related field with publications in A* conferences
- Hands on experience contributing to large scale LLM or Multi Modal pre training runs on large distributed servers with thousands of NVIDIA GPUs
- Familiarity with large scale distributed training frameworks, libraries, and tools
- Deep knowledge of state of the art transformer and non transformer modifications
- Strong expertise in PyTorch and Hugging Face libraries
Full Job Posting
About the Job
- 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 deep expertise in LLM and Multi Modal architectures, a strong grasp of pre training optimization, and a hands on, research driven approach.
Responsibilities
- Large Scale Pre Training: Conduct foundational pre training for LLMs and Multi Modal models on large distributed servers with thousands of NVIDIA GPUs.
- Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross modal alignment layers.
- Data Strategy: Source, filter, and curate massive scale textual and multi modal datasets, establishing robust data pipelines.
- Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies.
- Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency and computational performance.
- System Scalability: Contribute to the advancement of distributed training systems for seamless scalability and hardware efficiency.
Requirements
- A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, with good publications in A* conferences.
- Hands on experience contributing to large scale LLM or Multi Modal pre training runs on large distributed servers 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.
- Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment.
About Tether
- Tether is pioneering a global financial revolution with cutting edge solutions for digital tokens.
- Our product suite includes USDT, Tether Power, Tether Data, Tether Education, and Tether Evolution.
- We have a global remote team and are leaders in the fintech space.
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