Applied Scientist II, Regulatory Intelligence Safety & Compliance

We are seeking an exceptional Applied Scientist to join a team of experts in the field of machine learning, and work together to tackle challenging problems across diverse compliance domains. We leverage and train state-of-the-art multi-modal and large-language-models (LLMs) to detect illegal and unsafe products across the Amazon catalog. We work on machine learning problems for multi-modal classification, intent detection, information retrieval, anomaly and fraud detection, and generative AI.

This is an exciting and challenging position to deliver scientific innovations into production systems at Amazon-scale to make immediate, meaningful customer impacts while also pursuing ambitious, long-term research. You will work in a highly collaborative environment where you can analyze and process large amounts of image, text and tabular data. You will work on hard science problems that have not been solved before, conduct rapid prototyping to validate your hypothesis, and deploy your algorithmic ideas at scale. There will be something new to learn every day as we work in an environment with rapidly evolving regulations and adversarial actors looking to outwit your best ideas.


Key job responsibilities
• Design and evaluate state-of-the-art algorithms and approaches in multi-modal classification, large language models (LLMs), intent detection, information retrieval, anomaly and fraud detection, and generative AI
• Translate product and CX requirements into measurable science problems and metrics.
• 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.

A day in the life
- Understanding customer problems, 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
- 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


About the team
We are a team of applied scientists building AI/ML solutions to make Amazon Earth’s most trusted shopping destination for safe and compliant products.