Senior Applied Scientist, AGI Customization

Requirements

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, CA, Sunnyvale - 192,200.00 - 260,000.00 USD annually
• USA, MA, Cambridge - 167,100.00 - 226,100.00 USD annually
• USA, WA, BELLEVUE - 167,100.00 - 226,100.00 USD annually

Additional Information

The Artificial General Intelligence (AGI) Customization Team is seeking a highly skilled and experienced Applied Scientist to support adoption and enable customization of Amazon Nova. The role focuses on developing state-of-the-art services and tools for model customization, including supervised fine-tuning, reinforcement learning, and knowledge distillation across large language models. As an Applied Scientist, you will play a important role in developing advanced customization capabilities that enable enterprises to build highly performant application-specific models without the need for training models from scratch. Your work will directly impact how companies leverage Amazon Nova models for their specific use cases. Key job responsibilities - Contribute to the development of novel customization techniques including extended post-training, continued pre-training, and advanced knowledge distillation - Collaborate with cross-functional teams to design and implement enterprise-ready tooling for various training techniques on Amazon SageMaker - Design and execute experiments to optimize model accuracy, latency, and cost across different customization approaches (SFT, DPO, PPO) - Develop and enhance preference learning algorithms and training curricula for customer-specific applications - Create robust evaluation frameworks for assessing model performance across different domains and use cases - Contribute to the development of the Responsible AI toolkit, including creating training and evaluation datasets for model alignment - Design and implement secure access mechanisms for early model checkpoints and weights - Communicate technical insights and results to both technical and non-technical stakeholders through presentations and documentation

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