Applied Scientist, Industry Builder Team
About Amazon Web Services Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 105 Availability Zones within 33 geographic regions, with announced plans for 18 more Availability Zones and 6 more AWS Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.
AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and consistent support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow.
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 10,000 local startups and has provided cloud skills training to over 700,000 talents. Amazon’s first two utility-scale renewable projects—a solar farm and a wind farm—are also generating clean energy to the country’s 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
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
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and consistent support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow.
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 10,000 local startups and has provided cloud skills training to over 700,000 talents. Amazon’s first two utility-scale renewable projects—a solar farm and a wind farm—are also generating clean energy to the country’s 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
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
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Basic Qualifications
- 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
Preferred Qualifications
- 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