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Description

Amazon Regulatory Intelligence Safety and Risk (RISC) team mission is to protect customers from products that are unsafe, illegal, illegally marketed, controversial or otherwise in violation of Amazon’s policies while enabling our Selling Partners to offer their broadest selection of safe and compliant products. We achieve these objectives worldwide by: (1) taking a science-first approach to offer trustworthy listings to our customers, (2) inventing intuitive and precise tools to simplify our selling partners’ compliance journey and (3) innovating to reduce our cost to serve.

The RISC Science team is seeking an experienced Applied Scientist to leverage AI/ML technique, embracing deep learning knowledge, self-learning algorithm, and ensemble learning technique, to improve worldwide Amazon operation efficiency and empower our selling partners to offer the broadest selection of safe and compliant products. We work on machine learning problems of 1) multi-modal classification with data sparsity issue to improve Amazon business efficiency; 2) implicit risk detection to proactively mitigate compliance risks; 3) document understanding using LLMs to enrich deterministic risk signals; 4) information retrieval through OCR engine; 5) generative AI to streamline the processes.

This demanding role involves implementing scientific innovation into Amazon-scale production ML systems, achieving significant worldwide impacts while also engaging in ambitious, long-term research. You have the opportunity to analyze and process large amount of structured (tabular, graph) and unstructured data (image, document) in a highly collaborative environment. By developing and deploying the state-of-the-art deep learning and self-learning multi-modality and large language models (LLMs), you will tackle large-scale classification, process improvement, document understanding, information retrieval, and generative AI challenges. Join our expert team to deploy cutting-edge AI/ML solutions, improving Amazon business efficiency and simplifying our selling partners' compliance journey.

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

About the team

We are a team of scientists and engineers building AI/ML solutions to improve Amazon business efficiency and simplify our selling partners' compliance journey.

Basic Qualifications

• 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

Preferred Qualifications

• Experience with modality agnostic representation

• Experience with federated learning

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.