AI Research Engineer Pre training Remote

About the Job

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 LLM architectures, 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 that lead to groundbreaking advancements: data curation, strengthening baselines, identifying and resolving existing pre‑training bottlenecks to push the limits of AI performance.

• 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.