Senior Applied Scientist, Data-Tech, Workplace Health & Safety

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 artificial intelligence (AI) to build advanced solutions that make workplaces safer and more efficient? If so, we invite you to join our WHS Data-Tech team as a Senior Applied Scientist and take your career to the next level.

At WHS Data-Tech, we leverage computer vision (CV), large language models (LLMs), and AI-driven innovations to develop industry-leading solutions that proactively enhance workplace safety. Our work spans real-time risk assessment, analytics, and AI-powered insights, all aimed at creating a safer work environment at scale.

As a Senior Applied Scientist specializing in CV and LLMs, you will play a pivotal role in shaping our next-generation safety solutions. You’ll lead the innovation, design and implementation of AI-powered features that redefine workplace safety. Your work will drive strategic decisions, optimize system architecture, and influence best practices, ensuring our technology remains pioneering.

If you are not sure that every qualification listed describes you perfectly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skill-sets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Key job responsibilities
• Invent and apply novel CV and LLM approaches to analyze complex, multimodal data. Distinguish problems that require fundamentally new solutions from those addressable through existing methods.
• Architect and implement core scientific components of CV and LLM-based solutions. Deploy scalable, production-grade models across on-device and cloud environments in collaboration with engineering teams.
• Design evaluation frameworks and conduct rigorous experiments. Benchmark against state-of-the-art and iterate on model architectures to advance performance.
• Stay at the forefront of computer vision, LLMs, and generative AI research. Proactively identify and prototype novel techniques for integration into production solutions.
• Collaborate with product managers, program managers, and business stakeholders to translate ambiguous problems into well-scoped ML solutions with measurable impact.
• Build consensus on complex projects and decompose them into independent workstreams. Identify and resolve technical bottlenecks that limit innovation.
• Propose research initiatives, secure leadership alignment, and present technical trade-offs and strategic recommendations to stakeholders.
• Author internal technical documents and research papers. Contribute to the scientific community through conferences and peer reviews.
• Actively mentor team members, elevating scientific capabilities and fostering a culture of innovation and rigorous inquiry.

About the team
WHS Data-Tech's charter is to deliver technology solutions and data insights that help reduce workplace risks and injuries at Amazon. Our customers are Worldwide (WW) Operations and the WHS organization. The technology solutions landscape for WHS Data-Tech includes applications built using native Amazon Web Services (AWS) technologies and device-software hybrid solutions that leverage generative artificial intelligence (AI), computer vision, sensors, and Internet of Things (IoT) technologies. The team also conducts scientific research and modeling to generate safety insights, and provides analytical solutions ranging from senior leadership deliverables to business-unit-specific reports.

Basic qualifications

- Experience with neural deep learning methods and machine learning
- Experience programming in Java, C++, Python or related language
- 3+ years of building machine learning models for business application experience
- PhD in computer science, computer engineering, or related field
- 5+ years of applied research experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Formal PhD degree in computer science, computer engineering, or related field can be replaced by five years of relevant experience on top of a MSc, and seven years on top of a BSc

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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