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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 architectures and a strong grasp of pre-training optimization with a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms leading to groundbreaking advancements, including data curation, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of AI performance.
Responsibilities:
- Conduct pre-training of 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.
Requirements:
- 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.
- 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.
Language Requirements: Excellent English communication skills.
