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.

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
Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment.

AI Research Engineer – Pre training

Key skills