Applied Scientist, Regulatory, Intelligence, Safety and Compliance (RISC)

Requirements

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience building machine learning models or developing algorithms for business application
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
• Experience in professional software development
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Additional Information

RISC's vision is to make Amazon Earth's most trusted shopping destination for safe and compliant products. We do this by protecting customers from products that are unsafe, illegal, illegally marketed, controversial or otherwise in violation of Amazon's policies while enabling our Selling Partners (SPs) to offer their broadest selection of safe and compliant products. We are seeking an exceptional Applied Scientist to join a team of experts in the field of agentic AI, GenAI, Machine Learning, Software Engineers, and work together to tackle challenging problems across diverse compliance domains. We leverage and train state-of-the-art large-language-models (LLMs), multi-modal model, mixed with elegant harness engineering and SKILL building to 1) detect illegal and unsafe products across the Amazon catalog; 2) automation safety and compliance content authoring; 3) reasoning over enforcement action to provide actionable insights to Amazon sellers. We work on machine learning problems for content generation, multi-modal classification, global product taxonomy, intent detection, information retrieval, anomaly and fraud detection, agentic AI, generative AI and multi-agent system. 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, unstructured and tabular data. You will work on challenging 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 content generation, multi-modal classification, global product taxonomy, intent detection, information retrieval, anomaly and fraud detection, agentic AI, generative AI and multi-agent system. - 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

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