ECCV: Where does computer vision go from here?

Amazon Scholar Thomas Brox sees promise in unsupervised learning, generative models, and integrating machine learning and geometry.

The European Conference on Computer Vision (ECCV), which began on Sunday, is held every other year, alternating with the International Conference on Computer Vision (ICCV). Scheduled for Glasgow this year, ECCV has, like most of the summer’s major computer science conferences, gone virtual.

Thomas Brox
Thomas Brox, an Amazon Scholar and professor of computer science at the University of Freiburg.

Together with CVPR (the IEEE Conference on Computer Vision and Pattern Recognition), ICCV and ECCV round out the big three of computer vision conferences.

“In the past, ECCV tended to be a bit more on math and 3-D geometry than CVPR, which was always a bit more in the direction of pattern recognition,” says Thomas Brox, an Amazon Scholar and professor of computer science at the University of Freiburg, who is a program chair at this year’s ECCV. “But since, these days, everything is pattern recognition and deep learning, they’re more similar.”

Brox’s first ECCV was in 2004, when he was still a graduate student, so he had been attending for 10 years when the deep-learning revolution in computer vision began.

“I like it if things get simpler,” Brox says. “So I liked that time a lot — 2014, 2015, this was when many computer vision problems simplified a lot suddenly. You took a network — it didn’t matter much what — and you always got a much better performance than everyone else got before.

“Of course, now everyone has done that, and it’s getting pretty complicated again. It’s about changing a few details in your network, how you train, how you collect the data, how you present it, and then you get your little incremental improvements.

“Progress on benchmarks is still relatively fast, but progress on concepts is relatively slow. In the past, when that’s been the case, then at some point progress on the benchmarks has stopped, too. During my postdoc in 2010, it was the same situation with object detection. There was a lot of progress before that, and then it became slower and slower, and no one had a good idea of what to do. And then deep learning came and solved the problem, more or less.”

Ten years later, with deep learning, too, “there’s very little conceptual novelty,” Brox says. “I think we’re hitting a wall.”

Getting a foothold

Of course, no one knows what the next conceptual breakthrough will be: “If anyone knew, we would all be doing it,” Brox says with a laugh. But he’s willing to hazard a few guesses.

“One bet might be that you want to go a bit away from these label annotations, because they might actually limit you more than they help,” Brox says. Today, most machine learning is supervised, meaning that it involves labeled training examples, and the machine learning model learns to predict the labels on the basis of input features. Training with unlabeled data is known as unsupervised.

Amazon Scholars

The Scholars program is designed for academics who want to apply research methods in practice and help us solve hard technical challenges without leaving their academic institutions. Learn more about the program here.

“As soon as you work on unsupervised losses, then you’re back in the old days, in a way,” Brox says. “We formulated the same kinds of losses. But there was no deep network, and the optimization techniques worked directly on the output variables rather than the network parameters. It needs something else on top, but something in this direction can be interesting.”

Another technique that intrigues Brox is the use of generative models, rather than the discriminative models that prevail today. Given two variables — say, visual features of images and possible labels of objects in those images — discriminative models, such as today’s neural nets, estimate the value of one variable given a specific value of the other: if the image feature is a pointy ear, the label is likely to be “cat”.

A generative model, by contrast, attempts to learn a probability distribution that relates all possible values of one variable to all possible values of the other. As such, it offers a statistical model of the world, rather than a bag of tricks for performing classifications.

“Discriminative models just try to find features that separate two different classes, and you’re happy if you can discriminate them,” Brox says. “Whereas with generative models, you also want to explain what you see. If you can explain what you see, then you have potentially much more robust models that also generalize better.

“That is a direction that is very interesting, but it is not currently competitive. When you come with a new concept, it will typically not be as good as all these well-optimized methods that optimize all the details. It’s similar to what the deep-learning people faced for many years, when they were already convinced that they had the right tool, but no one in the computer vision community wanted to believe them, because their numbers were much worse. You really have to believe in your strategy to go on with it and make it better until you hit the state of the art.”

Geometry returns

In his own work, Brox is also investigating the possibility of integrating deep learning with the “math and 3-D geometry” that used to be a priority at ECCV. In particular, he’s looking at using the motion of objects to infer information about their 3-D shape, an approach that would seem to benefit from rigorous computational methods for correlating points on an object’s surface under different rotations.

“In the motion signal, there’s a good deal of information, and it’s not well used in the current techniques,” Brox says. “Especially when you think of unsupervised learning, this might be quite useful. Making better use of 3-D structure has also been one of my interests.

“At the beginning, nobody believed that 3-D vision could be captured by deep learning. Everybody thought, ‘Okay, these two fields are incompatible, so if you work on 3-D vision, you’re safe.’ You don’t need to shift to deep learning.

“Actually, that’s not true. There are also benefits if you use deep learning for 3-D vision. But you can’t do everything with deep learning there. It’s more a mixture of classical geometry, classical math, and using deep learning for the pattern recognition parts of it. The combination of both is quite promising.”

Research areas

Related content

US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), Evaluation, etc. Your work will directly impact our customers in the form of novel products and services .
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Scientist with fabrication and data analysis experience working on all elements of a superconducting circuit. The position is on-site at our lab, located on the in Pasadena, CA. The ideal candidate will have had prior experience building software tools for data analysis and visualization to enable deep diving into fabrication details, electrical test data. We are looking for candidates with strong engineering principles, resourcefulness and data science experience. Organization and communication skills are essential. Key job responsibilities * Develop and automate data pipeline pertinent to superconducting device fabrication. * Develop analytical tools to uncover new information about established and new processes. * Develop new or contribute to modifying existing data visualization tools. * Utilize machine learning to enable better deeper dives into fabrication and related data. * Interface with various software, design, fabrication and electrical test teams to enable new functionalities. A day in the life The role will be vital to the fabrication team and quantum computing device integration mechanism. The candidate will develop software based analytical tools to enable data driven decisions across projects related to fabrication and supporting infrastructure. Each fabrication run delivers additional data. The candidate will stay close to the details of fabrication providing data analysis and quick feedback to key stakeholders. At the end of fabrication runs custom and standardized reports will be generated by the candidate to provide insights into data generated from the run. This position may require occasional weekend work. About the team 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 Here at AWS, 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 (gender diversity) 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. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
CA, ON, Toronto
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As the Data Science Manager on this team, you will: - Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity. - Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Science Manager in Machine Learning, you will: - Directly manage and lead a cross-functional team of Applied Scientists, Data Scientists, Economists, and Business Intelligence Engineers. - Develop and manage a research agenda that balances short term deliverables with measurable business impact as well as long term investments. - Lead marketplace design and development based on economic theory and data analysis. - Provide technical and scientific guidance to team members. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment - Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. - Develop science and engineering roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Collaborate with business and software teams across Amazon Ads. - Stay up to date with recent scientific publications relevant to the team. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, CA, Sunnyvale
The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Applied Scientist with strong machine learning background to build state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality - Research in advanced customer understanding and behavior modeling techniques - Collaborate with cross-functional teams of scientists, engineers, and product managers to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification - Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results - Think Big on conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports About the team The AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.
US, NY, New York
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Why you love this opportunity Amazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities Key job responsibilities As an Applied Scientist III on this team you will: * Lead complex and ambiguous projects to deliver bidding recommendation products to advertisers. * Build machine learning models and utilize data analysis to deliver scalable solutions to business problems. * Perform hands-on analysis and modeling with very large data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience. * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production. * Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders. * Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's marketplace. * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. * Research new predictive learning approaches for the sponsored products business. * Write production code to bring models into production. * Mentor junior scientists and engineer in the team.
CA, ON, Toronto
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Why you love this opportunity Amazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities As an Applied Scientist on this team you will: * Build machine learning models and utilize data analysis to deliver scalable solutions to business problems. * Perform hands-on analysis and modeling with very large data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience. * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production. * Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders. * Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's marketplace. * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. * Research new predictive learning approaches for the sponsored products business. * Write production code to bring models into production.
US, NJ, Newark
At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As Senior Data Scientist, you will build scalable solutions and models to support our business functions (Marketing, Product, Content). Leveraging a range of methods including machine learning and simulation, you will explain, quantify, predict and prescribe in support of informing critical business decisions. You will translate business goals into agile, insightful analytics. You will seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders. ABOUT THE TEAM Audible data science team partners with marketing, content, product, and technology teams to solve business and technology problems using scientific approaches to build product and services that surprise and delight our customers. We employ scalable cutting-edge machine learning (ML), causal inference (CI) and GenAI / Natural Language Processing (NLP) knowledge to better target customers and prospects, understand and personalize the content, and context needed to optimize their book-listening experience. We operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects. ABOUT YOU We are looking for a motivated, results-oriented Data Scientist with strong rigor and demonstrable skills in ML, CI, NLP, data mining and/or large-scale distributed computation. As a Senior Data Scientist, you will... - Develop and validate models to optimize the Who, When, Where and How of all our interactions with customers - Develop Amazon-scale data engineering pipelines - Imagine and invent before the business asks, and create groundbreaking applications using cutting-edge approaches - Develop compelling data visualizations - Work closely with other data scientists, ML experts, engineers as well as business across globe, and on cross-disciplinary efforts with other scientists within Amazon - Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. Our Hub+Home hybrid workplace model gives employees the flexibility between gathering in a common office space (work from hub) and remote work (work from home). For more information, please visit adbl.co/hybrid
US, WA, Seattle
Are you excited by the idea of developing algorithms to improve the shopping experience for Amazon customers? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art modeling techniques? Join us and you'll help make the shopping experience better for millions of customers while also advancing the state of Amazon's science through publishing research! Key job responsibilities - Develop and apply new machine learning algorithms - Use expertise in supervised learning and causal inference to improve ML performance - Scale optimization techniques to drive business value - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Present and publish science research, contributing to Amazon's science community - Mentor junior engineers and scientists. - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.