Amazon

DESCRIPTION

About Amazon Regulatory Intelligence, Safety, and Compliance (RISC).

Amazon RISC’s vision is to make Amazon the Earth’s most trusted shopping destination for safe and compliant products. Towards this mission, we take a science-first approach to building technology, products and services, that protect customers from unsafe, illegal, controversial, or policy-violating products while offering the optimal selling partner experience.

Job Summary

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

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 images, texts, documents, 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 generative AI, agentic system, multi-modal classification, intent detection, information retrieval, anomaly and fraud detection.
• 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 scientists and engineers building AI/ML solutions to make Amazon the Earth’s most trusted shopping destination for safe and compliant products.

BASIC QUALIFICATIONS

• PhD, or Master's degree with 2+ years of machine learning experience, or bachelor degree with 3+ years of machine learning experience
• Experience programming in Python, Java, C++, or related language
• Experience with neural deep learning methods, LLM, and natural language processing
• Experience with conducting research in a corporate setting

PREFERRED QUALIFICATIONS

• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.