CVPR: Deep learning has more gas in the tank

Amazon’s Larry Davis on the past and future of computer vision research.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), which will be held virtually this year and starts next week, is the major conference in the field of computer vision. But when Larry Davis, senior principal scientist for Amazon Fashion, started attending it, “computer vision” wasn’t even part of its title.

“I think 1981 was my first time having a formal role at the conference,” Davis says. “Back then the meeting was called Pattern Recognition and Image Processing. My advisor [Azriel Rosenfeld] was pretty much regarded as the founder of the field of computer vision, but when he founded it, he called it ‘picture processing’. Then he and a couple other senior people decided to rename it computer vision, and they changed the name of the conference to CVPR.”

In his four decades attending CVPR, Davis has witnessed several waves of change sweep through the computer vision field.

Larry Davis.jpeg
Larry Davis, senior principal scientist for Amazon Fashion.

“The 1980s was the decade of what we would call segmentation,” Davis says. “How do you take a picture and break it into parts? One of the motivations was computational — trying to reduce the combinatorics of finding objects in pictures. What’s an object? You might say it’s some connected set of pixels. There are a lot of connected subsets of pixels in images. You can’t begin to look for objects by enumerating the connected subsets. But suppose you could break the image up into 100 pieces via color and texture segmentation. Now you can begin to think of a more-or-less brute-force algorithm that would look at combinations of pieces and ask, ‘Is this the right shape for a dog?’

“Alternatively, you could try to outline objects. Perceptual psychologists will tell you that most of the information that people perceive from images is from the boundaries of objects or the boundaries between the objects and background. So people tried for decades to build edge detectors that would enclose objects. That kind of died out. People weren’t making much progress.

“In the ’90s there was an interest in geometry — multiview geometry, how do you build 3-D models from multiple images from a camera moving through the environment. There was a ton of great work on that.

“In the 2000s, computers became fast enough, mass storage became cheap enough, that people were doing video, so there was a lot of interest in video surveillance. Once the social-media companies took hold, then the emphasis in video was more on consumer videos, being able to caption them, summarize them visually, index into them, all kinds of things.

“And then, the last seven or eight years it’s been all deep learning for all these problems. And that’s when the field took off in terms of size and sophistication.”

Paradigm shift

As it has in other areas of AI, deep learning has revolutionized computer vision.

“Instead of sitting around trying to design new feature detectors, you design new architectures that learn the features,” Davis says.

For researchers of Davis’s generation, that sometimes meant the abandonment of long-standing research programs.

“I saw in a number of my colleagues a resistance to the deep-learning wave,” Davis says. “I met [deep-learning pioneer] Geoff Hinton and a researcher then at Hopkins named Terry Sejnowski — he was more a biomedical type who was interested in neural computing — when they were young. I always thought they were brilliant. It wasn’t what I did, but I was willing to believe them. So it never really bothered me to vacate classical computer vision and just parachute into deep-learning territory. And in any event, any faculty members who didn’t do it were dragged in by their students, because they certainly knew what kind of skills they needed to develop in order to be successful.”

For all of deep learning’s successes, however, some researchers have begun to question whether we can expect to get much more mileage out of it.

“I think we might have hit a wall on some problems — for example, there have been no dramatic improvements recently in the ability to detect objects in pictures,” Davis says. “If you want to build the world’s ultimate flower detector, that never makes a mistake, that sees every flower from any angle, even when only a very small part of the flower is visible, or its image is very small, you realize that might never happen. Even if the problem is just the unavailability of training data to cover all edge cases — if they could ever be known — the cost of doing this for all object types, maybe even just for flowers, would be prohibitive today.”

It never really bothered me to vacate classical computer vision and just parachute into deep-learning territory. ... I think the field attracts people who are not challenged by change.
Larry Davis

Davis, however, remains optimistic about the continued applicability of deep-learning techniques. While object recognition may be running up against its limits, he says, “the interesting thing is that there are still so many more problems that you can successfully approach. They’re just different problems, and even the current object detectors are good enough to solve lots of important problems.”

Within Amazon Fashion, for instance, “there are several different things going on that involve different types of deep-learning architecture, and they all have some scientific creativity, something new that somebody’s developed,” Davis says.

Indeed, three of the ten Amazon papers at CVPR this year concern ways in which computer vision can help improve customers’ experience while shopping for clothing online.

“The number of deep-learning problems whose solution would improve our customers’ shopping experience is enormous, and we’ve expanded our science team to tackle more and more of them,” Davis says. “We’re always looking for more ML scientists excited about fashion to join us.”

Still, if some new, more powerful artificial-intelligence paradigm arises, Davis will remain no more attached to deep learning than he was to pixel clustering and boundary detection.

“In computer science generally, things change very rapidly,” he says. “You can’t hold on to old stuff. I think the field attracts people who are not challenged by change.”

Research areas
About the Author
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

Related Content

Related content

Work with us

See more jobs
US, WA, Redmond
Are you interested in building and driving the technical vision, strategy, and implementation for Kuiper’s LEO Capacity Management Services? Kuiper is hiring a Principal Data Scientist to help lead the analysis, definition and implementation of our global, highly reliable, predictive data driven services that manage the end-to-end resources of Kuiper’s Internet Service for ground and constellation networks.A day in the lifeYou will partner with Product Management, customers, RF, Networking and Beam Planning engineers to understand all capabilities and designs of the Kuiper ISP. You will drive the data driven models of bandwidth, latency and customer segment consumption to create highly reliable, time sensitive, predictive capacity management systems that drive overall monetization and customer experience.An ideal candidate will have analytical, data science, and system engineering skills to model the interdependent business and technical processes needed to operate and expand a world-wide fleet of space communication and ground assets. The candidate will use these models to enhance customer delight by meeting performance agreements, faster decisions, reduced costs, and simplified interactions.About the hiring groupThe Team is responsible for Architecture, design and delivering and end to end Networking systems for both constellation and ground, as well as the services that utilize the network to delivery last mile and and back-haul internet services. This includes extreme scale of global Software Defined Network and Capacity ManagementJob responsibilitiesWe are looking for a Principal Data Scientist on this team. You will be responsible for identifying, scoping, and delivering capacity planning solutions with a focus on Europe; based on a deep understand of your customers' needs, you will work closely with senior leaders, scientists, engineers, and business teams worldwide to develop and implement advanced mathematical and economic models and algorithms. You will identify data and science-related bottlenecks, anticipate and make trade-offs, balance business needs versus scientific and technical complexity and constraints, and guide and manage escalations, collaborating closely with multiple teams to ensure the relevance and impact of your work to business stakeholders.You will need an ability to take large, scientifically complex projects and break them down into manageable hypotheses, design meaningful research questions and analyze the resulting data to inform functional specifications, and then deliver features in a successful and timely manner. You excel at being a thought leader as we chart new courses with our capacity planning technologies, and at defining a vision for products in early stages. Maturity, high judgment, negotiation skills, and the ability to influence and earn the trust of senior leaders are essential to success in this role.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, CA, Sunnyvale
Are you a passionate scientist in the area of computer vision and machine learning who is aspired to develop new and innovative technologies to new product categories? Are you interested in applying your deep knowledge to new and challenging areas? Are you looking to scale capabilities computer vision and machine learning capabilities to new workload sizes? Are you up to the task of delivering innovative and scalable technology that manages automated recognition of millions of items?You will be part of a passionate team whose missions is to push the frontier of computer vision and machine learning technology into the smart home application area. This is a great opportunity for you to innovate in this space by developing algorithms at the edge and in the cloud, and integrating them into consumer services to enable a premium customer experience. In this role, you will be an owner of the full algorithm development cycle, from sensor evaluation and data engineering to algorithm design, implementation, optimization and deployment. This position also requires experience with developing efficient software components on resource-constrained computing platforms on the edge. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms.Main Responsibilities· Apply best practices to investigate, acquire, process and analyze data sources for algorithm development.· Research and implement the state-of-the-art methods in computer vision and machine learning to deliver algorithms that meets product specifications.· Design, build algorithm evaluation frameworks, schedule and report algorithm performance on a regular basis.· Optimize and deploy algorithms on target hardware platforms.· Establish, develop and maintain frameworks and procedures for image sensor selection and evaluation and image quality monitoring.· Influence system design by making informed decisions on the selection of data sources, algorithms and sensors.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
RO, Timisoara
The Amazon Devices team designs and engineers consumer electronics,including the best-selling Ring cameras, Kindle family of products, Firetablets, Fire TV, Amazon Dash, and Amazon Echo.As an Applied Scientist, you will participate in the design,development, and evaluation of models and machine learning (ML)technology to delight our customers. More specifically, as a member of the team, you will be involved in researching state ofthe art Computer Vision (CV) & ML solutions for Amazon devices and cloud services.You will be part of a team delivering features that are well received byour customers.
RO, Timisoara
The Amazon Devices team designs and engineers consumer electronics,including the best-selling Ring cameras, Kindle family of products, Firetablets, Fire TV, Amazon Dash, and Amazon Echo.As an Applied Scientist, you will participate in the design,development, and evaluation of models and machine learning (ML)technology to delight our customers. More specifically, as a member of the team, you will be involved in researching state ofthe art Computer Vision (CV) & ML solutions for Amazon devices and cloud services.You will be part of a team delivering features that are well received byour customers.
US, WA, Seattle
How to use the world’s richest collection of e-commerce data to improve payments experience for our customers? Amazon Consumer Payments Global Data Science team seeks a Data Scientist for building analytical solutions that will address increasingly complex business questions in the North America Credit space.Amazon.com has a culture of data-driven decision-making and demands insights that are timely, accurate, and actionable. This team provides a fast-paced environment where every day brings new challenges and new opportunities.As a Data Scientist in this team, you will be driving the analytics roadmap and will provide descriptive and predictive solutions to the North America Credit business team through a combination of data mining techniques as well as use statistical and machine learning techniques for segmentation and prediction. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.Responsibilities· Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard regression and classification techniques such as Random Forest, XGBoost package, Keras framework· Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management· Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area· Innovate by adapting new modeling techniques and procedures· You should be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets· You should have exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive· You will extract huge volumes of data from various sources and message streams and construct complex analyses. You will implement data flow solutions that process data real time on message streams from source systems· You should be detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion.· You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions· Your teams will work with distributed machine learning and statistical algorithms upon a large Hadoop cluster to harness enormous volumes of online data at scale to serve our customers
IN, KA, Bangalore
Advertising at Amazon is a fast-growing business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. The Ad Optimization group in Bangalore has the charter to build data-science focused products and platforms for Amazon Advertising. One of our key focus areas is Traffic Quality where we endeavor to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace. We do this by building machine learning and optimization algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic. The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion.Twitch is a strategic video supply source for Amazon Advertising and we need to innovate invalid traffic algorithms to counter the specific risks for Twitch, saving advertisers hundreds of millions of dollars of wasted spend.Traffic quality systems process billions of ad-impressions and clicks per day. by leveraging cutting-edge open source technologies like Hadoop, Spark, Redis and Amazon's cloud services like EC2, S3, EMR, DynamoDB and RedShift. We build and deploy complex machine learning and advanced optimization algorithms that operate at scale. We are looking for talented applied scientists who enjoy working on creative algorithms and thrive in a fast-paced, fun environment. An Applied Scientist is responsible for solving complex big-data problems in the online advertising space using data mining, machine learning, statistical analysis and computational economics. An ideal candidate should have strong depth and breadth knowledge in machine learning, data mining and statistics. The candidate should have reasonable programming and design skills to manipulate unstructured and big data and build prototypes that work on massive datasets. The candidate should be able to apply business knowledge to perform broad data analysis as a precursor to modeling and to provide valuable business intelligence.
IN, KA, Bangalore
Advertising at Amazon is a fast-growing business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. The Ad Optimization group in Bangalore has the charter to build data-science focused products and platforms for Amazon Advertising. One of our key focus areas is Traffic Quality where we endeavor to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace. We do this by building machine learning and optimization algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic. The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion. We are committed to building a long-term traffic quality solution that encompasses all Amazon advertising channels and provides state-of-the-art traffic filtering that preserves advertiser trust and saves them hundreds of millions of dollars of wasted spend.Traffic quality systems process billions of ad-impressions and clicks per day. by leveraging cutting-edge open source technologies like Hadoop, Spark, Redis and Amazon's cloud services like EC2, S3, EMR, DynamoDB and RedShift. We build and deploy complex machine learning and advanced optimization algorithms that operate at scale. We are looking for talented applied scientists who enjoy working on creative algorithms and thrive in a fast-paced, fun environment. An Applied Scientist is responsible for solving complex big-data problems in the online advertising space using data mining, machine learning, statistical analysis and computational economics. An ideal candidate should have strong depth and breadth knowledge in machine learning, data mining and statistics. The candidate should have reasonable programming and design skills to manipulate unstructured and big data and build prototypes that work on massive datasets. The candidate should be able to apply business knowledge to perform broad data analysis as a precursor to modeling and to provide valuable business intelligence.
US, MA, Cambridge
We’re looking for a passionate, talented, and inventive Senior Applied Scientist to help build industry-leading technologies in speech translation. Our team's mission is to enable Alexa to break down language barriers for our customers.Job responsibilitiesAs a Senior Applied Scientist with the Alexa Artificial Intelligence (AI) team, you will be responsible for developing novel algorithms that advance the state-of-the-art in language processing and entity resolution, driving model and algorithmic improvements, formulating evaluation methodologies and for influencing design and architecture choices. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to build novel products and services that make use of speech and language technology. You will work in a hybrid, fast-paced organization where scientists and engineers work together and drive improvements to production. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, IL, Chicago
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, VA, Arlington
Managing trillions of objects in storage, retrieving them in sub-x ms, new features that deploy to hundreds of thousands of hosts, achieving 99.999999999% durability. These are just a few of the numbers that give you a sense of the scale of the exciting problems you will find every day working in Simple Storage Service (S3). Amazon S3 powers businesses across the globe that make the lives of consumers better daily. Whether its electronic content delivered to your home, technology that betters your remote working experience, allows you to plan travel to exotic places or simply get stuff delivered to your home. As a Data Scientist in S3, you will be able to dive deep into some of the most interesting and complex problems at the largest scale.We are seeking an innovative and technically strong data scientist with a background in performance optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment.This role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs. Our employees and the neighboring community will also benefit from the associated investments from the Commonwealth including infrastructure updates, public transportation improvements, and new access to Reagan National Airport.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Palo Alto
** This job can be based in Seattle or Palo Alto **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!Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.As a Senior Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you will own systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses.As a Senior Applied Scientist on this team you will:· Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects.· Develop real-time algorithms to allocate billions of ads per day in advertising auctions.· Lead technical efforts within this team and across other teams.· Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.· 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.· Work closely with software engineers to assist in productionizing your ML models.· Research new machine learning approaches.· Recruit Applied Scientists to the team and act as a mentor to other Scientists on the team.Impact and Career Growth:In this role you will have significant impact on this team as well as drive cross team projects that consist of Applied Scientists, Data Scientists, Economists, and Software Development Engineers. This is a highly visible role that will help take our products to the next level. You will work alongside many of the best and brightest science and engineering talent and the work you deliver will have a direct impact on customers and revenue!Why you love this opportunity:Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
This early-stage initiative is tackling problems that span a variety of domains: computer vision, machine learning, Human-Computer-Interaction and real-time systems.As a Computer Vision Applied Scientist you will help solve a variety of technical challenges and mentor other team members while actively following our entrepreneurial culture that encourages us to wear many hats. You will play an active role in leading translation of business and functional requirements into concrete deliverables. Build quick prototypes and proofs of concept in partnership with other technology leaders within the team. Improve products and concepts as a direct result of your research while delivering significant benefits to business.You are well versed in the relevant literature and will have the ability to find, understand and adapt new state of the art methods from the core discipline as well as beyond to create breakthrough problem-solution pairs. You will tackle challenging, novel situations every day and you’ll have the opportunity to work with other technical teams at Amazon within and outside the organization. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that haven’t been dreamed up and much less solved at scale before - anywhere. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people.Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Sunnyvale
Our Alexa Product Advisor (part of Alexa Shopping) vision is to provide the best possible answers for a wide range of questions around product being asked by the customer. The first step in providing these answers is to form high quality classification and machine understanding of natural language questions into their core components (shape, product references, attributes, pronouns etc).Alexa Shopping is looking for an experienced Sr Data Scientist to be a part of a team solving complex natural language processing problems and customer demand insights (including segmentation analysis and personas building using big data, ML and potentially AI). This is a blue-sky role that gives you a chance to roll up your sleeves and dive into big data sets in order to build simulations and experimentation systems at scale, build optimization algorithms and leverage cutting-edge technologies across Amazon. This is an opportunity to think big about how to solve a challenging problem for the customers and understand their requirements for products.You will work closely with product and technical leaders throughout Alexa Shopping and will be responsible for influencing technical decisions in areas of development/modelling that you identify as critical future product offerings. You will identify both enablers and blockers of adoption for product understanding, and build programs to raise the bar in terms of understanding product questions and predict the shaping of customer utterances as we move from simple to complex utterances.The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.
US, WA, Seattle
Amazon Performance Advertising sits at the intersection of e-commerce and advertising, developing new native advertising experiences that are a critical area of strategic focus for the company. Amazon Sponsored Brands is an always-on advertising product that amplifies brand content to shoppers researching on Amazon through prominent cost-per-click ads. As we move up the purchase funnel, and create more touch points for shoppers, Sponsored Brands plays a key role in the discoverability and reach of brand content. Amazon Sponsor Brands is looking for a scientist to lead innovation for our global advertising marketplace. At the heart of our advertising business are systems for optimizing the ad marketplace involving sourcing and targeting, experimentation infrastructure, AI methods for inference and control, as well as metrics-driven closed loop optimizations.Job Responsibilities:· Design, develop, and productionize end-to-end machine learning solutions.· Work closely with software engineers and data scientists on detailed requirements, technical designs and implementation of end-to-end solutions in production· Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving· Provide technical leadership, research new machine learning approaches to drive continued scientific innovation· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences· Help attract, lead, and mentor technical talent.Impact and Career Growth:· You will invent new shopper and advertiser experiences, and accelerate the pace of Machine Learning and Optimization.· Influence customer facing shopping experiences to helping suppliers grow their retail business and the auction dynamics that leverage native advertising, this role will be powering the engine of one the fastest growing businesses at Amazon.· Define a long-term science vision for our ad marketplace, driven fundamentally from the needs of our customers, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding.Why you love this opportunity:Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
US, VA, Arlington
We are developing advanced technologies that enhance the experience of shoppers in physical stores. Designed and custom-built by Amazon, existing products such as the Amazon Dash Cart and Amazon Go integrate a variety of advanced technologies including computer vision, sensor fusion, and advanced machine learning.We are seeking talented people who want to join an ambitious research and development program that who will help us define the future of physical retail. As a Principle Applied Scientist, you will architect systems that involve addressing complex technical challenges while meeting stringent performance and robustness goals. You will mentor other engineers and ensure they are producing state-of-the-art solutions while also continuing to advance their knowledge and capabilities. You will play an active role in guiding the development of research infrastructure for real-time machine learning and you will play a key role in translating technology solutions into real-world systems in partnership with other product organizations.You will tackle challenging situations every day and you’ll have the opportunity to work with multiple technical teams at Amazon. You should be comfortable with a high degree of ambiguity and have the communication skills necessary to articulate strategic approaches that both address immediate product needs and which set your team up for future success. Along the way, we guarantee that you’ll learn a lot, have fun, and make a positive impact on many customersIn this role:• You will encounter challenging, open-ended technical problems to solve, with solutions that span the entire implementation stack from devices to real-time algorithms to cloud-based services.• You will be able to demonstrate your strong technical and communication skills as well as your leadership abilities to deliver brand new customer experiences on behalf of Amazon.• You must be curious in the face of ambiguity and able to develop prototypes and perform experiments to create robust, scalable, distributed and fault-tolerant features to enhance the experience of Amazon customers around the globe.NOTE re: LOCATION: The main location for this job is the Washington DC metro region.
US, WA, Seattle
Within Alexa Shopping, we are forming a new, insurgent team to better serve Alexa customers who are less engaged with Amazon stores. This is a large cohort of customers who want to use Alexa as their Shopping assistant and we want to meet the particular needs of these customers. The first job is to understand these customers better, how they shop in and out of Amazon stores, how they use Alexa and Alexa Shopping features, and what gaps remain between their needs and the features we have today.A day in the lifeYou will use data science to better understand how we can fulfill Alexa Shopping’s aspirations to be the anywhere shopping AI for with a focus on customers who are less engaged with Amazon. In the first months on the job, you will hire a new team and undertake a deep dive on this customer cohort using data on Amazon shopping behaviors, Alexa feature usage and primary research on their needs. This deep dive will lead to specific marketing program, feature and product ideas. You will influence the most senior leaders in the Alexa Shopping organization to steer marketing and feature roadmaps in order to better serve these customers. In the first year, you will write several PRFAQs for new feature and product ideas and you will advocate for them at the highest levels of the organization.About the hiring groupAt Alexa Shopping, we strive to enable shopping in everyday life through technology innovations in both voice and screen applications, from organizing shopping to covering in the moment shopping needs. We allow customers to create, add to, and collaborate on shopping lists that can be used in any store and to instantly order whatever they need, by simply interacting with their Smart Devices such as Echo or Fire TV. Our products range from package tracking to shopping lists and instant ordering, and with these capabilities we seek to make advertising more actionable and useful to customers. The business is both focused on generating value for shoppers as well as advertisers.Job responsibilities· Use data science to understand the shopping behaviors and needs of Alexa customers who are less engaged in Amazon stores· Hire and develop a nimble team of insurgents· Identify marketing and product gaps between our current feature portfolio and the needs of this customer cohort· Develop and experiment with product, feature and program ideas (e.g. write PRFAQs) and advocate for them at the highest levels of leadership in the organizationAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Bellevue
Join us in a historic endeavor to make Computer Vision accessible to the world with breakthrough research!The AWS Computer Vision science team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops.AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world.Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, and 3D scene and layout understanding.We are located in Seattle, Pasadena, Palo Alto, Atlanta (USA) and in Haifa and Tel Aviv (Israel).Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office. You will be responsible for developing and disseminating customer-facing personalized recommendation algorithms. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization.You will design deep learning algorithms that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside more senior team members to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization.Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide.Key responsibilities· Develop deep learning algorithms for high-scale recommendations problem· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement.· Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency.· Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.