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.

Job 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.

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