Applied Scientist, Artificial General Intelligence

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in deep learning to build industry-leading technology with Large Language Models (LLMs) and Multimodal systems.

As part of the AGI team, the Applied Scientist will collaborate closely with talented colleagues to lead the development of advanced approaches and modeling techniques, driving forward the frontier of LLM technology. This includes innovating model-in-the-loop and human-in-the-loop approaches to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. The Applied Scientist will also have a direct impact on enhancing customer experiences through state-of-the-art products and services.

An Applied Scientist with the AGI team will support the science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience, while setting examples for the team on good science practices and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to implement algorithms and models effectively.

The ideal candidate should be passionate about delivering experiences that delight customers and creating robust solutions. They will also create reliable, scalable, and high-performance products that require exceptional technical expertise and a sound understanding of Machine Learning.

• PhD, or Master's degree with 4+ years of experience in building machine learning models or developing algorithms for business applications
• Experience programming in Java, C++, Python, or related languages
• Experience with neural deep learning methods and machine learning
• Experience with Large Language Models (LLMs) or multimodal systems

• PhD in Mathematics, Statistics, Engineering, Machine Learning, Computer Science, or related discipline
• 4+ years of industry or postdoctoral experience in machine learning
• Experience with patents or publications at top-tier peer-reviewed conferences or journals
• Experience with popular deep learning frameworks such as MxNet, PyTorch, or TensorFlow
• Experience in building large-scale machine learning systems
• Proficiency in state-of-the-art Natural Language Processing (NLP) and Computer Vision (CV) deep learning models

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