Senior Applied Scientist, AGI Customization

About the role

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 an 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.

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

Basic Qualifications

• 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

Preferred Qualifications

• 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.

Pay

• 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