Applied Scientist, Workplace Health & Safety

Amazon•Bellevue, WA

3d•$136,000 - $223,400

About The Position

Are you driven by the challenge of solving complex problems that directly impact the safety and well-being of millions of Amazon Associates worldwide? Do you want to push the boundaries of AI to build innovative solutions that make workplaces safer and more efficient? If so, we invite you to join our WHS DataTech team as an Applied Scientist and take your career to the next level! At WHS DataTech, we leverage Large Language Models (LLMs), Computer Vision, and AI-driven innovations to develop industry-leading solutions that proactively enhance workplace safety. Our work spans real-time risk assessment, predictive analytics, and AI-powered insights, all aimed at creating a safer work environment at scale. As an Applied Scientist specializing in LLMs and Computer Vision, you will play a pivotal role in shaping our next-generation safety solutions. You’ll be at the forefront of innovation, designing and implementing AI-powered features that redefine workplace safety. Your work will drive strategic decisions, optimize system architecture, and influence best practices, ensuring our technology remains industry-leading. WHS DataTech is a multidisciplinary team of scientists and engineers dedicated to building AI-powered solutions that improve workplace safety across Amazon. We work at the intersection of large-scale data, advanced machine learning, and computer vision, delivering innovations that enhance decision-making, streamline operations, and protect millions of associates worldwide. Our collaborative culture emphasizes scientific rigor, engineering excellence, and a strong mission focus on creating safer, more efficient workplaces. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 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. 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 $223,400/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.

Requirements

3+ years of building models for business application experience
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
PhD degree in Computer Science or Computer engineering
A formal PhD degree in Computer Science or Computer engineering or can be replaced by 5 years of relevant experience on top of a MSc, and 7 years on top of a BSc.
Experience using Unix/Linux
Experience in professional software development

Responsibilities

Apply LLM model to analyze complex unstructured datasets and extract meaningful insights.
Collaborate with software engineers to implement and deploy machine learning (LLM or CV) solutions.
Conduct experiments and evaluate model performance, iterating and improving as needed.
Stay up-to-date with the latest advancements in machine learning and related fields.
Collaborate with cross-functional teams to understand business needs and identify areas for application of machine learning.
Present findings and recommendations to stakeholders and contribute to the overall research and development strategy.