Applied Scientist, Industry Builder Team

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Amazon Web Services came to China in 2013 and has been relentlessly investing and expanding our infrastructure and business since then. Amazon Web Services launched its China (Beijing) Region (operated by Sinnet) in September 2016 and its China (Ningxia) Region (operated by NWCD) in December 2017. In 2019 Amazon Web Services added a new region in Hong Kong making China the only country with three Amazon Web Services regions aside from the U.S. In 2022 Amazon Web Services launched Local Zone in Taipei. Amazon Web Services has also established an AI lab in Shanghai and two IoT labs in Shenzhen and Taipei. The Amazon Web Services Partner Network has thousands of Partners in China. Amazon Web Services has supported over 10000 local startups and has provided cloud skills training to over 700000 talents. Amazons first two utility-scale renewable projectsa solar farm and a wind farmare also generating clean energy to the countrys grid.

Amazon Web Services (AWS) is looking for an Applied Scientist to join the Industry Builder Team in China Mainland & Hong Kong (CMHK). The Industry Builder Team designs and builds industry assets and capabilities: reusable solution accelerators reference architectures prototypes and technical building blocks that address the highest-value use cases within a target industry (e.g. games manufacturing automotive retail media & entertainment). As an Applied Scientist on this team you are an Industry Builder who owns the AI/ML core of these industry assets. You will invent implement and productize generative AI and machine learning components that make our industry assets differentiated scalable and ready for customers to adopt. You own the AI portion of the industry asset lifecycle end to end. You do not just prototype: you build assets take them through review so they are validated for reuse and you are accountable for the quality documentation and adoption of your assets. Because the team is measured by the business impact its assets generate building assets that are reusable well-documented and adopted by the field is a core part of the role.
This is a builder role at the intersection of science and industry. You will partner with industry specialists solutions architects product teams and partners to turn industry problems into concrete deployable AI capabilities then harden them into assets the field can reuse across many customers.


Key job responsibilities
Own the AI/ML and generative AI components of industry assets and capabilities: research design implement and productize state-of-the-art models agents and pipelines that power reusable industry solutions
Own the full lifecycle of your assets: build them take them through review so they are validated for reuse document them well and iterate based on field and customer feedback. Keep your assets documentation and information complete and current so they are discoverable reusable by the field and their business impact can be accurately tracked
Translate prioritized industry use cases into technical building blocks (solution accelerators reference implementations sample code and prototypes) that the field and partners can adopt at scale. Collaborate with industry specialists to shape the industry solution roadmap contributing the science point of view on which AI capabilities are feasible differentiated and high-impact
Build rapid prototypes and minimum viable solutions in real customer engagements then generalize the learnings into reusable assets. Apply generative AI techniques (prompt engineering RAG agentic workflows fine-tuning model hosting and deployment) and classical ML where appropriate to solve never-before-solved industry problems
Create technical collateral (best-practice guides tutorials blog posts sample code workshops and presentations) adapted to technical business and executive stakeholders and evangelize assets to field teams and customers


About the team
The Industry Builder Team focuses on industry solution innovation. We explore potential solutions and develop them into code assets to help empower our industry customers. Most of our solutions leverage agentic AI technology and are built on top of AWS cloud service building blocks.

Diverse Experiences
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- 3 years of building machine learning models for business application experience
- Experience in any of the following areas: algorithms and data structures parsing numerical optimization data mining parallel and distributed computing high-performance computing
- Experience applying theoretical models in an applied environment

- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience in professional software development
- Experience in patents or publications at top-tier peer-reviewed conferences or journals

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