The Artificial General Intelligence (AGI) team is seeking a dedicated skilled and innovative Applied Scientist with a robust background in deep learning to build industryleading technology with Large Language Models (LLMs) and Multimodal systems.
Key job responsibilities
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 modelintheloop and humanintheloop approaches to ensure the collection of highquality 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 stateoftheart products and services.
A day in the life
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 practice and standards. Besides theoretical analysis and innovation an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into practice.
The ideal candidate should be passionate about delivering experiences that delight customers and creating robust solutions. They will also create reliable scalable and highperformance products that require exceptional technical expertise and a sound understanding of Machine Learning.
PhD or Masters degree and 4 years of building machine learning models or developing algorithms for business application experience
Experience programming in Java C Python or related language
Experience with neural deep learning methods and machine learning
Experience with Large Language Models (LLMs) or multimodal systems
PhD degree 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 toptier peerreviewed conferences or journals
Experience with popular deep learning frameworks including MxNet PyTorch or TensorFlow
Experience in building largescale machine learning systems
Proficiency in stateoftheart Natural Language Processing (NLP) and Computer Vision (CV) deep learning models
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