AI Research Engineer (Pre-training - LLM & Multi-Modal)
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Key skills for this role
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.
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, establishing robust data pipelines
- 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 scalability and hardware efficiency
Requirements
- Deep expertise in Large Language Model (LLM) and Multi Modal architectures
- Strong grasp of pre training optimization
- Hands on, research driven approach
- Experience with large scale distributed training on multi node GPU clusters
- Ability to design and prototype innovative architectures
- Experience with data sourcing, filtering, and curation for large datasets
Full Job Posting
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 h
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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