Amazon Applied Scientist RISC 

United States, Washington, Seattle 

565479377

Key job responsibilities
• Design and evaluate state-of-the-art deep learning algorithms and approaches in multi-modal classification, large language models (LLMs), self-learning and federated learning.
• Extend and invent new algorithms and scientific approaches that improve on the state-of-the-art to decrease Amazon’s cost to serve.
• Identify and drive scientist productivity improvements across science teams.
• Collaborate with product and tech partners and customers to validate hypothesis, drive adoption, and increase business impact.
• Key author in writing high quality scientific papers in internal and external peer-reviewed conferences.
• Lead cross-organization working groups to develop science foundational capabilities applicable to multiple use cases beyond compliance for the broader Customer Trust and Selling Partner Services (SPS) organizations.A day in the life
• Understanding stakeholder problem, existing process limitation/bottleneck, project timelines, and team/project mechanisms
• Proposing science formulations and brainstorming ideas with team to solve business problems
• Writing code, and running experiments with re-usable science libraries
• Reviewing labels and audit results with investigators and operations associates
• Sharing science results with science, product and tech partners and customers
• Partnering with internal and external tech teams to deploy model artifacts in production at scale
• Writing science papers for submission to peer-review venues, and reviewing science papers from other scientists in the team.
• Contributing to team retrospectives for continuous improvements
• Driving science research collaborations and attending study groups with scientists across Amazon

- PhD, or Master's degree with 5+ years of applied research experience
- Experience programming in Java, Python, C++ or related language
- Experience with neural deep learning and self-learning methods
- Experience with modality agnostic representation
- Experience with ML system design
- Experience with conducting research in a corporate setting

- Experience with modality agnostic representation
- Experience with federated learning