AI Research Engineer (Pre-training - LLM & Multi-Modal)
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About the Role
Drive innovation in architecture development for large-scale pre-training of Large Language Models and Multi-Modal systems, enhancing model intelligence and efficiency.
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Overview
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
- Large-Scale Pre-Training: 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 & thousands of NVIDIA GPUs.
- Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers to enhance model intelligence and multi-modal understanding.
- Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines for efficient pre-training.
- Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.
- Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs.
- System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms.
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