Social
Caltech banner image
Earlier this year, eight scientists were named as 2021 Amazon AI4Science Fellows at Caltech. The program is a result of a collaboration between Caltech and Amazon focused on supporting the AI4Science initiative, and its goal of sharing advances in artificial intelligence and machine learning.
Machine learning

Caltech names eight AI4Science fellows supported by Amazon

Amazon is collaborating with Caltech to support research, education, and outreach programs that help build bridges between AI and other areas of science and engineering.

By Douglas Gantenbein
April 30, 2021
6 min read
Share

In January, eight scientists were named as 2021 Amazon AI4Science Fellows at Caltech, the world-renowned science and engineering research institution based in Pasadena, Calif. The Fellows program is a result of a collaboration between Caltech and Amazon around supporting the AI4Science initiative and its goal of sharing the latest advances in artificial intelligence (AI) and machine learning (ML) to benefit all branches of scientific and engineering research.

There’s a huge level of demand among graduate students and post-doctoral scholars across campus to leverage the techniques and advantages afforded by using AI and machine learning in their own research.
Yisong Yue

“Amazon’s gift will have a real impact on our program,” said Yisong Yue, professor of Computing and Mathematical Sciences and co-founder of the AI4Science initiative. “There’s a huge level of demand among graduate students and post-doctoral scholars across campus to leverage the techniques and advantages afforded by using AI and machine learning in their own research.”

“Caltech is uniquely placed to be a leader in AI4science given its interdisciplinary collaborations and small size,” added Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences and co-founder of the AI4Science initiative. “We thank Amazon for supporting this groundbreaking initiative.”

For those students specializing in data sciences, Yue said, “it allows them to be exposed to the datasets and research questions faced by scientists in other fields, as well as in companies such as Amazon, and to push the boundaries of existing AI and machine-learning algorithms to effectively address these questions.”

“At Amazon, we strongly believe that AI should be an integral part of all science, not just computer science,” said Marzia Polito, AWS senior manager of applied science. “We are building products supporting the use of AI in many fields of science and many science-based business applications. We are thrilled to collaborate with Caltech, a world-class institution doing ground-breaking science work, and we are honored to have a chance to contribute to their amazing results. There could be no better evidence toward a future where AI and science thrive together.”

This year’s cohort of Fellows comes from the Division of Biology and Biological Engineering, the Division of Chemistry and Chemical Engineering, and the Division of Engineering and Applied Science, reflecting the broad range of research being explored. The Fellows will apply cutting-edge ML and AI techniques to research areas ranging from neural prosthetics and theoretical chemistry, to the human microbiome.

Caltech 4 reduced size
Earlier this year, eight scientists were named as 2021 Amazon AI4Science Fellows at Caltech. The program is a result of a collaboration between Caltech and Amazon focused on supporting the AI4Science initiative, and its goal of sharing advances in artificial intelligence and machine learning.
Credit: Glynis Condon

The Amazon AI4Science Fellows and research areas are:

  • Carmen Amo Alonso, a graduate student pursuing her PhD in control and dynamical systems in the lab of John Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and Bioengineering, is studying the application of AI to large-scale complex systems in areas as diverse as understanding the gut microbiome to designing optimized “smart” grids.
  • Sara Beery, a graduate student pursuing her PhD in computing and mathematical sciences in the lab of Pietro Perona, Allen E. Puckett Professor of Electrical Engineering, is developing approaches to collect global-scale, real-time ecological data for conservation purposes.
  • Charles Guan, a graduate student pursuing his PhD in bioengineering in the lab of Richard Andersen, James G. Boswell Professor of Neuroscience, T&C Chen Brain-Machine Interface Center Leadership Chair, and director of the T&C Brain-Machine Interface Center, is developing decoding algorithms for neural prostheses and studying the neural basis of movement.
  • Kadina Johnston, a graduate student pursuing her PhD in bioengineering in the lab of Nobel-prize winner Frances Arnold, Linus Pauling Professor of Chemical Engineering, Bioengineering, and Biochemistry and director of the Donna and Benjamin M. Rosen Bioengineering Center, is applying deep learning techniques to the directed evolution approach for enzyme engineering.
  • Nikola Kovachki, a graduate student pursuing his PhD in applied and computational mathematics in the lab of Andrew Stuart, Bren Professor of Computing and Mathematical Sciences, is using deep learning techniques for solving partial differential equations, which has applications for climate modeling, understanding turbulent flow of fluids, and a wide range of materials science problems.
  • Zhuoran Qiao, a graduate student pursuing his PhD in chemistry in the lab of professor of chemistry Tom Miller, is developing physics-based machine learning methods for studying problems in complex chemical systems.    
  • Guannan Qu, a post-doctoral scholar working under the supervision of Steven Low, Frank J. Gilloon Professor of Computing and Mathematical Sciences and Electrical Engineering, and Adam Wierman, professor of computing and mathematical sciences and director of Information Science and Technology, is developing multi-agent reinforcement learning approaches that are scalable, distributed, and have provable guarantees such that they will be usable for safety-critical applications.
  • He Sun, a post-doctoral scholar working under the supervision of Katie Bouman, assistant professor of computing and mathematical sciences, electrical engineering and astronomy and Rosenberg Scholar, is developing new machine learning algorithms that can improve current computational imaging approaches.

The diverse interests of the Fellows reflect a trend in AI and machine learning that emphasizes cross-disciplinary research and is in line with the AI4Science focus.

“Today we need students who are experts in their own fields, but who also can communicate across different fields,” Yue said. “Effective communication requires a mix of fundamentally sound rigorous thinking coupled with a broad perspective on how to apply that approach to wide range of domains, in order to reveal and distill the core technical challenges that underlie the pressing problems facing science and engineering research today.”

The AI4Science Fellows program is one component within a larger collaboration between Amazon and Caltech. In the area of AI4Science, Amazon is also providing funds for Caltech’s Carver Mead New Adventures Fund, AWS credits for research projects across campus, and support for AI4Science events to build community and share research results.

Beyond the AI4Science initiative, Caltech and Amazon are collaborating in a number of other areas. A group of scientists within AWS’s Amazon Rekognition team is a part of Caltech’s Innovation Center, a building near campus that houses Caltech corporate collaborators and startups. In 2019, AWS and Caltech also announced a collaboration around quantum computing, which will include the AWS Center for Quantum Computing, set to open later this year.

“Caltech prides itself as being an incubator for concepts like AI4Science and next-generation technologies like quantum computing,” said Caltech’s Kaushik Bhattacharya, vice provost for Research and Howell N. Tyson, Sr., Professor of Mechanics and Materials Science. “Our small size, our relentless focus on the most challenging scientific questions — along with our close-knit community that is interdisciplinary by design — encourage and enable us to always be pioneering in the application of tools and techniques from other fields to all areas of science and engineering. To help make sure our findings have impacts beyond academia, we greatly value our collaborations with partners like Amazon.”

Research areas
Tags
About the Author
Douglas Gantenbein
Douglas Gantenbein is a contributor to Amazon Science.

Related content

Work with us

See more jobs See more jobs
Applied Scientist II, Central Machine Learning
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques
Applied SCI III - AMZ007408
US, NY, New York
MULTIPLE POSITIONS AVAILABLE Employer: Amazon Development Center U.S., Inc. Offered Position: Applied Scientist III - AMZ007408 Job Location: New York, NY Position Responsibilities: Participate in the design, development, evaluation, deployment, and updating of formal reasoning systems for security, privacy, and data protection applications. Drive technical and scientific innovation in security automation, data protection, and privacy-preserving technologies, with a focus on developing scalable solutions for cloud environments. Develop and/or apply formal verification techniques and automated theorem proving methods for different applications in cloud security and privacy. Collaborate with internal and external users to understand requirements and enhance formal verification and automated reasoning capabilities. Lead research and development efforts in AI security, specifically evaluate emerging threats and opportunities, including securing Generative AI systems and designing robust safeguards. Proactively identify and explore new opportunities for deploying and leveraging formal reasoning solutions across various domains.
Applied Scientist, AWS Automated Reasoning
GB, London
The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking an applied scientist with broad interest and expertise in model checking, interactive theorem proving, programming language semantics, and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities Work with customer teams to understand the nature of their software and the properties they need to establish of it. Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. Use techniques spanning property-based testing to model checkers, and interactive theorem provers to establish program properties. Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team The Agentic Automated Reasoning Group at AWS develops and applies state of the art formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications, with a strong focus on AI based agents. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.
Member of Technical Staff, FAR (Frontier AI & Robotics)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to lead key initiatives in robotic intelligence. As a Member of Technical Staff, you'll spearhead the development of breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, science understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for specific research initiatives, ensuring robust performance in production environments - Mentor and support fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus A day in the life - Develop and implement novel foundation model architectures, working hands-on with our extensive compute infrastructure - Guide and support fellow scientists in solving complex technical challenges, from sim2real transfer to efficient multi-task learning - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster - Mentor team members while maintaining significant hands-on contribution to technical solutions Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through ground breaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Zappos Data Scientist II, Zappos/Shopbop Catalog Engineering
US, WI, Madison
As a Data Scientist on the Shopbop/Zappos Catalog Tech team, you will design and implement scientific approaches to revolutionize how we manage and enhance our product catalog data for our world-class selection of Shoes, Kids, and Active wear. You will work with Zappos' Senior leadership team to solve complex data challenges through advanced analytics and machine learning - creating innovative solutions and influencing product decisions through data-driven insights. You will lead critical initiatives to reduce catalog errors, accelerate product data capture, and develop state-of-the-art image classification systems for fashion features. You will partner daily with engineering teams and business stakeholders to provide expert guidance on model selection and implementation. As a member of the Zappos technical staff, you will leverage machine learning technologies and have access to industry leaders in AI/ML and E-Commerce to help grow your expertise. You will also routinely collaborate with data science teams across our sister companies at Amazon.com and Shopbop.com. You will push the boundaries of what's possible with applied machine learning and bring innovative solutions to bear for customers (including computer vision, NLP, and advanced ML models). You will think big about how data science can transform our catalog operations and be persistent in delivering robust, scalable solutions. Key job responsibilities Design and implement machine learning approaches to improve catalog data quality. Develop and validate scientific methodologies for automated data capture and classification. Partner with engineering teams to integrate ML models into production systems. Create and present analysis that drives decision-making at the senior leadership level. A day in the life You start the day reviewing model performance metrics, noting some drift in the image classification system that needs investigation. You spend the morning developing a new approach to reduce product attribute errors using recent advances in LLMs. In the afternoon, you meet with engineering teams to advise on model architecture for a new feature, and wrap up by analyzing the results of your latest A/B test on data capture efficiency improvements. About the team Zappos/Shopbop Catalog Tech team owns the software that drives our photostudio, product cataloging, and integration to Amazon's marketplace. We use Amazon's Leadership Principals and Engineering Expertise but have our own fun vibe. We are located in Madison WI, and Las Vegas NV.
Sr. Applied Scientist, Special Projects
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As an Applied Scientist on our team, you will focus on building state-of-the-art ML models for biology. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. Key job responsibilities - Build, adapt and evaluate ML models for life sciences applications - Collaborate with a cross-functional team of ML scientists, biologists, software engineers and product managers
Research Scientist II, WW Sustainability
US, WA, Seattle
Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices. Sustainability Science and Innovation (SSI) is a multi-disciplinary team within WW Sustainability combining science, analytics, economics, statistics, machine learning, product development, and engineering expertise. We use data across the sustainability imperatives (carbon, water, waste, biodiversity, environmental risk and more) and these skills and capabilities to identify, develop, experiment, and scale the scientific solutions and innovations necessary for Amazon, customers and partners to help them solve their hardest unmet and evolving sustainability needs and goals. The Worldwide Sustainability (WWS) organization is seeking an exceptional scientific leader to join Amazon's Sustainability Science and Innovation team as a Researcher Scientist for Materials Chemistry Innovation. This role focuses on hands-on experimental research in materials chemistry to accelerate the discovery and validation of sustainable materials through systematic synthesis, characterization, and performance testing. You will lead the design and execution of experimental research campaigns targeting catalysts, functional materials, and sustainability-relevant chemistries across multivariate parameter spaces. You will establish scientific strategy and technical roadmaps for materials discovery while leading research initiatives that tackle complex sustainability challenges in critical industrial sectors. This position requires driving breakthrough solutions in materials synthesis and characterization through internal capabilities and strategic partnerships with universities, industry scientists, and government laboratories. You will mentor junior scientists and engineers while collaborating across Amazon's Innovation Lab Network to translate research into scalable solutions. Your leadership will be essential in developing early-stage, cost-effective materials that address significant technical and economic challenges fundamental to Amazon's operations, requiring you to navigate complex trade-offs between immediate deliverables and long-term environmental impact. You will also shape how emerging automation and AI tools are applied to accelerate materials discovery workflows. The ideal candidate demonstrates extensive experience in materials synthesis, advanced characterization techniques, and systematic experimental design for performance validations. You must possess proven ability to lead cross-functional teams, establish research priorities, and drive scientific innovation from concept to implementation. Deep technical expertise in materials testing methods, combined with strategic vision for translating research into practical applications is essential. Experience with high-throughput and combinatorial experimental approaches to efficiently explore large design spaces is highly valued. Your work will establish new paradigms in sustainable materials discovery through rigorous experimental research and performance testing, directly contributing to Amazon's sustainability goals while creating scalable solutions that extend beyond the company's immediate operations. Key job responsibilities - Develop scientific models that help solve complex and ambiguous sustainability problems, and extract strategic learnings from large datasets. - Work closely with applied scientists and software engineers to implement your scientific models. - Support early-stage strategic sustainability initiatives and effectively learn from, collaborate with, and influence stakeholders to scale-up high-value initiatives. - Support research and development of cross-cutting technologies for industrial decarbonization, including building the data foundation and analytics for new AI models. - Drive innovation in key focus areas including packaging materials, building materials, and alternative fuels. About the team Diverse Experiences: World Wide Sustainability (WWS) 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. Inclusive Team Culture: It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.
Senior Applied Scientist, Applied AI Solutions GTM
US, TX, Dallas
Amazon Web Services (AWS) Applied AI Solutions (AAIS) is on a mission to make AI real for enterprises. We build and deploy production AI solutions that drive measurable business outcomes at scale, bringing together applied scientists, AI architects, business development professionals, and GTM specialists to help customers move from AI experimentation to production impact. Within AAIS, the GTM Acceleration team activates the field, measures impact, and scales what works. We are the connective tissue between AAIS product and science teams and the worldwide field organization, ensuring our AI solutions reach customers effectively, that we quantify the value we deliver, and that we build repeatable motions that scale globally. We are looking for an Applied Scientist who will serve as a force multiplier across our customer engagement teams, building the analytical foundations, predictive models, and reusable tooling that power our go-to-market strategy. You will work at the intersection of data science, machine learning, and business strategy, building models that quantify our value proposition, and creating scalable analytical assets that accelerate every engagement. This is a highly visible, high-impact role where your work directly influences how we demonstrate and measure the value of AWS AI solutions for enterprise customers. You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with software engineers, product managers, and business stakeholders. You will identify the right methodology for each problem, whether that is a classical statistical approach, a modern deep learning technique, or a novel combination, and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect Customer initiatives and across the Applied AI solution portfolio, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide. If you thrive at the intersection of rigorous science and customer-facing impact and are energized by translating complex model outputs into business decisions, we want to talk to you. Key job responsibilities Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements, business decisions, and customer outcomes Work directly with customers during production pilots to build and deploy AI solutions that demonstrate measurable business value Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes Build ROI models, business case tools, and forecasting systems for demand prediction, capacity planning, workforce optimization, and value quantification Apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale, and partner with software engineers to productionize models with reliability, monitoring, and operational excellence Build and own customer analytics capabilities including segmentation (by size tier, AI adoption, product penetration, entitlement), usage trend analysis, propensity modeling, and foundational datasets combining service usage with sales data Create self-service analytics platforms and automated insight delivery mechanisms that enable leadership to pull strategic intelligence on demand Enable field teams with reusable analytical assets, diagnostic notebooks, benchmarking studies, and scalable tooling that accelerate customer engagements Own success metrics and create mechanisms to measure model performance, adoption, and business impact across customer cohorts Define strategic frameworks and GTM recommendations by segment, translating data patterns and market signals into actionable go-to-market motions and investment priorities Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations, operating as a shared resource across 2-3 teams simultaneously About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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.
Applied Scientist I, Sponsored Products and Brands Agent
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About the team SPB Agent team's vision is to build a highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across all experiences. The SPB-Agent is the central agent that interfaces with advertisers across Ads Console, Selling Partner portals (Seller Central, KDP, Vendor Central), and internal Sales systems. We identify high-impact opportunities spanning from strategic product guidance to granular optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including fine-tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers.
Data Science Manager, Customer Experience and Business Trends
US, WA, Seattle
Amazon's Customer Experience and Business Trends (CXBT) is seeking a Data Science Manager to lead a team of scientists and engineers within Benchmarking Economics Analytics and Measurement (BEAM). BEAM is a central analytics and science function that drives Amazon's quantification of CX improvement opportunities through comparative benchmarks, partnering with stakeholders across CXBT, business domain teams, Finance, SCOT, and other centralized science teams. This is a hands-on leadership role for a manager who can set technical direction, build durable data products, and grow people. You will own the strategy and roadmap for a portfolio of analytics products, working backward from leadership and stakeholder needs to deliver insights that inform decisions at the speed of business. Key job responsibilities - Build a holistic metrics and trend-detection product. Lead the team to design and operationalize an always-on framework of indicators that surfaces emerging business trends reliably enough to brief senior leaders. - Partner with cross-org stakeholders to drive product adoption and impact. Work directly with internal customers and partner teams to ensure our products are tightly aligned with business use cases, translate ambiguous problems into well-scoped analytics solutions, and drive adoption so that insights translate into decisions and measurable business impact. - Manage, mentor, and grow the team. Hire, develop, and retain a high-performing team of scientists and engineers. Set clear expectations, give actionable feedback, create stretch opportunities, and build the bench strength needed to scale the team's scope over time. - Lead the transformation from traditional analytics to a GenAI-native operating model. Shape and execute the team's technical strategy to evolve from manual, study-based analytics toward GenAI-enabled products and workflows — accelerating insight generation, improving self-serve access for stakeholders, and freeing capacity for deeper scientific investment.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social