Amazon
Senior Applied Scientist, Artificial General Intelligence
Amazon, Seattle, Washington, us, 98127
Job ID: 2802128 | Amazon.com Services LLC - A57The Artificial General Intelligence (AGI) team is looking for a Senior Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for Amazon, working with other acclaimed engineers and world-famous scientists.Key Job Responsibilities
Join us to work as an integral part of a team that has diverse experience with GenAI models in this space. We work on these areas:Scaling lawsHardware-informed efficient model architecture, low-precision trainingOptimization methods, learning objectives, curriculum designDeep learning theories on efficient hyperparameter search and self-supervised learningReasoning, self-reflection, interactive learning, data synthesisDistributed training methods and solutionsAbout the Team
The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.BASIC QUALIFICATIONS
PhD, or Master's degree and 5+ years of applied research experience3+ years of building machine learning models for business application experienceExperience with neural deep learning methods and machine learningExperience programming in Java, C++, Python or related languageExperience with deep learning frameworks such as Pytorch and JAX.PREFERRED QUALIFICATIONS
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 6+ years of industry or academic research experienceExperience with patents or publications at top-tier peer-reviewed conferences or journalsRelevant Generative Artificial Intelligence (GenAI) research experience with LLMs and multimodalitiesFor system researchers, familiarity with deep learning compilers, auto-parallelization, and XLA/MLIR ecosystemsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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Join us to work as an integral part of a team that has diverse experience with GenAI models in this space. We work on these areas:Scaling lawsHardware-informed efficient model architecture, low-precision trainingOptimization methods, learning objectives, curriculum designDeep learning theories on efficient hyperparameter search and self-supervised learningReasoning, self-reflection, interactive learning, data synthesisDistributed training methods and solutionsAbout the Team
The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.BASIC QUALIFICATIONS
PhD, or Master's degree and 5+ years of applied research experience3+ years of building machine learning models for business application experienceExperience with neural deep learning methods and machine learningExperience programming in Java, C++, Python or related languageExperience with deep learning frameworks such as Pytorch and JAX.PREFERRED QUALIFICATIONS
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 6+ years of industry or academic research experienceExperience with patents or publications at top-tier peer-reviewed conferences or journalsRelevant Generative Artificial Intelligence (GenAI) research experience with LLMs and multimodalitiesFor system researchers, familiarity with deep learning compilers, auto-parallelization, and XLA/MLIR ecosystemsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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