blueswarm image.png
Swarm robotics involves scores of individual mobile robots that mimic the collective behavior demonstrated by animals. Certain robots, like the Bluebot pictured here, perform some of the same behaviors as a school of fish, such as aggregation, dispersion, and searching.
Courtesy of Radhika Nagpal, Harvard University

Schooling robots to behave like fish

Radhika Nagpal has created robots that can build towers without anyone in charge. Now she’s turned her focus to fulfillment center robots.

When Radhika Nagpal was starting graduate school in 1994, she and her future husband went snorkeling in the Caribbean. Nagpal, who grew up in a landlocked region of India, had never swum in the ocean before. It blew her away.

“The reef was super healthy and colorful, like being in a National Geographic television show,” she recalled. “As soon as I put my face in the water, this whole swarm of fish came towards me and then swerved to the right.”

Meet the Blueswarm
Blueswarm comprises seven identical miniature Bluebots that combine autonomous 3D multi-fin locomotion with 3D camera-based visual perception.

The fish fascinated her. As she watched, large schools of fish would suddenly stop or switch direction as if they were guided by a single mind. A series of questions occurred to her. How did they communicate with one another? What rules — think of them as algorithms — produced such complex group behaviors? What environmental prompts triggered their actions? And most importantly, what made collectives so much smarter and more successful than their individual members?

Radhika Nagpal is a professor of computer science at Harvard University’s Wyss Institute for Biologically Inspired Engineering and an Amazon Scholar
Radhika Nagpal is a professor of computer science at Harvard University’s Wyss Institute for Biologically Inspired Engineering and an Amazon Scholar.

Since then, Nagpal, a professor of computer science at Harvard University’s Wyss Institute for Biologically Inspired Engineering and an Amazon Scholar, has gone on to build swarming robots. Swarm robotics involves scores of individual mobile robots that mimic the collective behavior demonstrated by animals, e.g. how flocks of birds or schools of fish move together to achieve some end. The robots act as if they, too, were guided by a single mind, or, more precisely, a single computer. Yet they are not.

Instead, they follow a relatively simple set of behavioral rules. Without any external orders or directions, Nagpal’s swarms organize themselves to carry out surprisingly complex tasks, like spontaneously synchronizing their behavior, creating patterns, and even building a tower.

More recently, her lab developed swimming robots that performed some of the same behaviors as a school of fish, such as aggregation, dispersion, and searching. All without a leader.

Nagpal’s work demonstrates both how far we have come in creating self-organizing robot swarms that can perform tasks — and how far we still must go to emulate the complex tapestries woven by nature. It is a gap that Nagpal hopes to close by uncovering the secrets of swarm intelligence to make swarm robots far more useful.

Amorphous computing

The Caribbean fish sparked Nagpal’s imagination because she was already interested in distributed computing, where multiple computers collaborate to solve problems or transfer information without any single computer running the show. At MIT, where she had begun her PhD program, she was drawn to an offshoot of the field called amorphous computing. It investigates how limited, unreliable individuals — from cells to ants to fish — organize themselves to perform often complex tasks consistently without any hierarchies.

Amorphous computing was “hardware agnostic.” This meant that it sought rules that guided this behavior in both living organisms and computer systems. It asked, for example, how identical cells in an embryo form all the organs of an animal, how ants find the most direct route to food, or how fish coordinate their movements. By studying nature, these computer scientists hoped to build computer networks that operated on the same principles.

I got excited about how nature makes these complicated, distributed, mobile networks. Those multi-robot systems became a new direction of my research
Radhika Nagpal

After completing her doctoral work on self-folding materials inspired by how cells form tissues, Nagpal began teaching at Harvard. While there, she was visited by her friend James McLurkin, a pioneer in swarm robotics at MIT and iRobot.

“James is the one that got me into robot swarms by introducing me to all the things that ant and termite colonies do,” Nagpal said. “I got excited about how nature makes these complicated, distributed, mobile networks. James was developing that used similar principles to move around and work together. Those multi-robot systems became a new direction of my research.”

She was particularly taken by Namibian termites, which build large-scale nest mounds with multiple chambers and complex ventilation systems, often as high as 8 feet tall.

“As far as we know, there isn’t a blueprint or an a priori distribution between who’s doing the building and who is not. We know the queen does not set the agenda,” she explained. “These colonies start with hundreds of termites and expand their structure as they grow.”

The question fascinated her. “I have no idea how that works,” she said. “I mean, how do you create systems that are so adaptive?”

Finding the rules

Researchers have spent decades answering that question. One way, they found, is to act locally. Take, for example, a flock of geese at a pond. If one or two birds on the outside of the flock see a predator, they grow agitated and fly off, alerting the next nearest birds. The message percolates through flock. Once a certain number of birds have “voted” to fly off, the rest follow without any hesitation. They are not following a leader, only reacting only to the birds next to them.

How dynamic circle formation works

The same type of local behaviors could be used to make driverless vehicles safer. An autonomous vehicle, Nagpal explains, does not have to reason about all the other cars on the road, only the ones around it. By focusing on nearby vehicles, these distributed systems use less processing power without losing the ability to react to changes very quickly.

Such systems are highly scalable. “Instead of having to reason about everybody, your car only has to reason about its five neighbors,” Nagpal said. “I can make the system very large, but each individual’s reasoning space remains constant. That’s a traditional notion of scalable —the amount of processing per vehicle stays constant, but we’re allowed to increase the size of the system.”

Another key to swarm behavior involves embodied intelligence, the idea that brains interact with the world through bodies that can see, hear, touch, smell, and taste. This is a type of intelligence, too, Nagpal argues.

It’s almost like each individual fish acts like a distributed sensor. Instead of me doing all the work, somebody on the left can say, ‘Hey, I saw something.’ When the group divides the labor so that some of us look out for predators while the rest of us eat, it costs less in terms of energy and resources.
Radhika Nagpal

“When you think of an ant, there is not a concentrated set of neurons there,” she said, referring to the ant’s 20-microgram brain. “Instead, there is a huge amount of awareness in the body itself. I may wonder how an ant solves a problem, but I have to realize that somehow having a physical body full of sensors makes that easier. We do not really understand how to think about that still.”

Local actions, scalable behavior, and embodied intelligence are among the factors that make swarms successful. In fact, researchers have shown that the larger a school of fish, the more successful it is at evading predators, finding food, and not getting lost.

“It’s almost like each individual fish acts like a distributed sensor,” Nagpal said. “Instead of me doing all the work, somebody on the left can say, ‘Hey, I saw something.’ When the group divides the labor so that some of us look out for predators while the rest of us eat, it costs less in terms of energy and resources than trying to eat and look out for predators all by yourself.

“What’s really interesting about large insect colonies and fish schools is that they do really complicated things in a decentralized way, whereas people have a tendency to build hierarchies as soon as we have to work together,” she continued. “There is a cost to that, and if we try to do that with that with robots, we replicate the whole management structure and cost of a hierarchy.”

So Nagpal set out to build robots swarms that worked without top-down organization.

Animal behavior

A typical process in Nagpal’s group starts by identifying an interesting natural behavior and trying to discover the rules that generate those actions. Sometimes, they are surprisingly simple.

Take, for example, some behaviors exhibited by Nagpal’s colony of 1,000 interactive robots, each the size of quarter and each communicating with its nearest neighbors wirelessly. The robots will self-assemble into a simple line with a repeating color pattern based on only two rules: a motion rule that allows them to move around any stationary robots, and a pattern rule that tells them to take on the color of their two nearest neighbors.

Other combinations of simple rules spontaneously synchronize the blinking of robot lights, guide migrations, and get the robots to form the letter “K.”

Most impressively, Nagpal and her lab used a behavior found in termites, called stigmergy, to prompt self-organized robot swarms to build a tower. Stigmergy involves leaving a mark on the environment that triggers a specific behavior by another member of the group.

Stigmergy plays a role in how termites build their huge nests. One termite may sense that a spot would make a good place to build, so it puts down its equivalent of a mud brick. When a second termite comes along, the brick triggers it to place its brick there. As the number of bricks increase, the trigger grows stronger and other termites begin building pillars nearby. When they grow high enough, something triggers the termites to begin connecting them with roofs.

“The building environment has become a physical memory of what should happen next,” Nagpal said.

Nagpal used that type of structural memory to prompt her robotic swarm to build a ziggurat tower. The instructions included a motion rule about how to move through the tower and a pattern rule about where to place the blocks. She then built some small, block-carrying robots that built a smaller but no less impressive structure.

Her lab developed a compiler that could generate algorithms that would enable the robots to build specific types of structures — perhaps towers with minarets — by interacting with stigmergic physical memories. One day, algorithm-driven robots could move sandbags to shore up a levee in a hurricane or buttress a collapsed building. They could even monitor coral reefs, underwater infrastructure, and pipelines — if they could swim.

Schooling robofish

From the start, Nagpal wanted to build her own school of robotic fish, but the hardware was simply too clunky to make them practical. That changed with the advent of smartphones, with their low-cost, low-power processors, sensors, and batteries.

In 2018, she got her chance when she received an Amazon Machine Learning Research Award. This allowed her to build Blueswarm, a group of robotic fish that performed tasks like those she observed in the Caribbean years ago.

Each Bluebot is just four inches long, but it packs a small Raspberry Pi computer, two fish-eye cameras, and three blue LED lights. It also has a tail (caudal) fin for thrust, a dorsal fin to move up or down, and side fins (pectoral fins) to turn, stop, or swim backward.

Bluebots do not use Wi-Fi, GPS, or external cameras to communicate their positions without error. Instead, she wants to explore what behaviors are possible relying only on cameras and local perception of one’s mates.

How multi-behavior search works

Researchers, she explained, find it difficult to rely only upon local perception. It has been difficult to tackle fundamental questions, like how does a robot visually detect other members of the swarm, how they parse information, and what happens when one member moves in front of another. Limiting Bluebot sensing to local perception forces Nagpal and her team to think more deeply about what robots really need to know about their neighbors, especially when data is limited and imprecise. 

Bluebots can mimic several fish school behaviors by tracking LED lights on the neighboring fishbots around them. Using 3D cameras and simple algorithms, they estimate distance between lights on neighboring fish. (The closer they appear, the further the fish.)

Nagpal’s seven Bluebots form a circle (called milling) by turning right if there is a robot in front of them. If there is no robot, they turn left. After a few moments, the school will be swimming in a circle, a formation fish use to trap prey.

They can also search for a target flashing red light. First, the school disperses within the tank. When a Bluebot finds the red LED, it begins to flash its lights. This signals the nearest Bluebots to aggregate, followed by the rest. If a single robot had to conduct a similar search by itself, it would take significantly longer.

These behaviors are impressive for robots, but represent a small subset of fish school behaviors. They also take place in a static fish tank populated by only one school of robot fish. To go further, Nagpal wants to improve their sensors and perhaps use machine learning to discover new rules that could be combined to produce the aquatic equivalent of a tower.

In the end, though, Nagpal does not want to build a better fish. Instead, she wants to apply the lessons she has learned to real-world robots. She is doing just that during a sabbatical working at Amazon, which operates the largest fleet of robots — more than 200,000 units — in the world.

Practical uses

Nagpal had little previous experience working in industry, but she jumped at the chance to work with Amazon.

“There are few others with hundreds of robots moving around safely in a facility space,” she said. “And the opportunity to work on algorithms in a deployed system was very exciting."

There are few others [like Amazon] with hundreds of robots moving around safely in a facility space. And the opportunity to work on algorithms in a deployed system was very exciting.
Radhika Nagpal

“The other factor is that Amazon’s robots do a mix of centralized and decentralized decision-making," she continued. "The robots plan their own paths, but they also use the cloud to know more. That lets us ask: Is it better to know everything about all your neighbors all the time? Or is it better to only know about the neighbors that are closer to you?”

Her current focus is on sortation centers, where robots help route packages to shipping stations sorted by ZIP codes. Not surprisingly, robots setting out from multiple points to dozens of different locations require a degree of coordination. Amazon’s robots are already aware of other robots. If they see one, they will choose an alternate route. But what path should they take, Nagpal asks. She wants to make sure those robots are making the most effective possible choices.

Cities already manage this. They limit access to some roads, change speed limits, and add one-way streets. Computer networks do it as well, rerouting traffic when packet delivery slows down.

Some of those concepts, such as one-way travel lanes, also work in sortation centers. They could act as stigmergic signals to guide robot behavior. She also believes there might be a way to create simple swarm behaviors that enable robots to react to advanced data about incoming packages.

Once her sabbatical is over, Nagpal plans to return to the lab. She wants to keep working on her Bluebots, improving their vision, and turning them loose in environments that look more like the coral reef she went snorkeling in 25 years ago.

She is also dreaming of swarms of bigger robots for use in construction or trash collection.

“Maybe we could do what Amazon is doing, but do it outside,” she said. “We could have swarms of robots that actually do some sort of practical task. At Amazon, that task is delivery. But given Boston’s snowstorms, I think shoveling the sidewalks would be nice.”

Research areas

Related content

US, CA, Santa Clara
About Amazon Health Amazon Health’s mission is to make it dramatically easier for customers to access the healthcare products and services they need to get and stay healthy. Towards this mission, we (Health Storefront and Shared Tech) are building the technology, products and services, that help customers find, buy, and engage with the healthcare solutions they need. Job summary We are seeking an exceptional Applied Scientist to join a team of experts in the field of machine learning, and work together to break new ground in the world of healthcare to make personalized and empathetic care accessible, convenient, and cost-effective. We leverage and train state-of-the-art large-language-models (LLMs) and develop entirely new experiences to help customers find the right products and services to address their health needs. We work on machine learning problems for intent detection, dialogue systems, and information retrieval. You will work in a highly collaborative environment where you can pursue both near-term productization opportunities to make immediate, meaningful customer impacts while pursuing ambitious, long-term research. You will work on hard science problems that have not been solved before, conduct rapid prototyping to validate your hypothesis, and deploy your algorithmic ideas at scale. You will get the opportunity to pursue work that makes people's lives better and pushes the envelop of science. Key job responsibilities - Translate product and CX requirements into science metrics and rigorous testing methodologies. - Invent and develop scalable methodologies to evaluate LLM outputs against metrics and guardrails. - Design and implement the best-in-class semantic retrieval system by creating high-quality knowledge base and optimizing embedding models and similarity measures. - Conduct tuning, training, and optimization of LLMs to achieve a compelling CX while reducing operational cost to be scalable. A day in the life In a fast-paced innovation environment, you work closely with product, UX, and business teams to understand customer's challenges. You translate product and business requirements into science problems. You dive deep into challenging science problems, enabling entirely new ML and LLM-driven customer experiences. You identify hypothesis and conduct rapid prototyping to learn quickly. You develop and deploy models at scale to pursue productizations. You mentor junior science team members and help influence our org in scientific best practices. About the team We are the ML Science and Engineering team, with a strong focus on Generative AI. The team consists of top-notch ML Scientists with diverse background in healthcare, robotics, customer analytics, and communication. We are committed to building and deploying the most advanced scientific capabilities and solutions for the products and services at Amazon Health. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA
US, WA, Seattle
We are designing the future. If you are in quest of an iterative fast-paced environment, where you can drive innovation through scientific inquiry, and provide tangible benefit to hundreds of thousands of our associates worldwide, this is your opportunity. Come work on the Amazon Worldwide Fulfillment Design & Engineering Team! We are looking for an experienced and senior Research Scientist with background in Ergonomics and Industrial Human Factors, someone that is excited to work on complex real-world challenges for which a comprehensive scientific approach is necessary to drive solutions. Your investigations will define human factor / ergonomic thresholds resulting in design and implementation of safe and efficient workspaces and processes for our associates. Your role will entail assessment and design of manual material handling tasks throughout the entire Amazon network. You will identify fundamental questions pertaining to the human capabilities and tolerances in a myriad of work environments, and will initiate and lead studies that will drive decision making on an extreme scale. .You will provide definitive human factors/ ergonomics input and participate in design with every single design group in our network, including Amazon Robotics, Engineering R&D, and Operations Engineering. You will work closely with our Worldwide Health and Safety organization to gain feedback on designs and work tenaciously to continuously improve our associate’s experience. Key job responsibilities - Collaborating and designing work processes and workspaces that adhere to human factors / ergonomics standards worldwide. - Producing comprehensive and assessments of workstations and processes covering biomechanical, physiological, and psychophysical demands. - Effectively communicate your design rationale to multiple engineering and operations entities. - Identifying gaps in current human factors standards and guidelines, and lead comprehensive studies to redefine “industry best practices” based on solid scientific foundations. - Continuously strive to gain in-depth knowledge of your profession, as well as branch out to learn about intersecting fields, such as robotics and mechatronics. - Travelling to our various sites to perform thorough assessments and gain in-depth operational feedback, approximately 25%-50% of the time. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
This single-threaded leader will focus on designing experiences and optimizations to monetize Amazon Detail Pages, while improving shopper experience and returns for our advertising customers. This leader will own generating different widgets (thematic, blended, interactive prompt, hybrid merchandising), and the science, tech and signaling systems to enable them for the different category and BuyX teams. This leader will also own science and systems for bidding into ranking systems like Percolate, and for operating the marketplace through allocation and pricing methods. They will own identifying operating points for WW marketplaces in terms of entitlement, RoAS impact and other benchmarks, plus invent ways to operationalize this thinking, all while experimenting to learn from the marketplace. The leader will also own AI generation of shopping pages for monetization (these shopping pages are built on DP content). We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, CA, Santa Monica
Amazon Advertising is looking for a motivated and analytical self-starter to help pave the way for the next generation of insights and advertising products. You will use large-scale data, advertising effectiveness knowledge and business information needs of our advertising clients to envision new advertising measurement products and tools. You will facilitate innovation on behalf of our customers through end-to-end delivery of measurement solutions leveraging experiments, machine learning and causal inference. You will partner with our engineering teams to develop and scale successful solutions to production. This role requires strong hands-on skills in terms of effectively working with data, coding, and MLOps. However, the ideal candidate will also bring strong interpersonal and communication skills to engage with cross-functional partners, as well as to stay connected to insights needs of account teams and advertisers. This is a truly exciting and versatile position in that it allows you to apply and develop your hands-on data modeling and coding skills, to work with other scientists on research in new measurement solutions while at the same time partner with cross-functional stakeholders to deliver product impact. Key job responsibilities As an Applied Scientist on the Advertising Incrementality Measurement team you will: - Create new analytical products from conception to prototyping and scaling the product end-to-end through to production. - Scope and define new business problems in the realm of advertising effectiveness. Use machine learning and experiments to develop effective and scalable solutions. - Partner closely with the Engineering team. - Partner with Economists, Data Scientists, and other Applied Scientists to conduct research on advertising effectiveness using machine learning and causal inference. Make findings available via white papers. - Act as a liaison to product teams to help productize new measurement solutions. About the team Advertising Incrementality Measurement combines experiments with econometric analysis and machine learning to provide rigorous causal measurement of advertising effectiveness to internal and external customers. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Boulder, CO, USA | New York, NY, USA | Santa Monica, CA, USA
US, CA, Santa Clara
Amazon launched the Generative AI Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team Here at AWS, it’s in our nature to learn and be curious about diverse perspectives. Our employee-led affinity groups foster a culture of inclusion that empower employees to feel 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. We have a career path for you no matter what stage you’re in when you start here. 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. We are open to hiring candidates to work out of one of the following locations: San Francisco, CA, USA | San Jose, CA, USA | Santa Clara, CA, USA
GB, London
Amazon Advertising is looking for a Data Scientist to join its brand new initiative that powers Amazon’s contextual advertising products. Advertising at Amazon is a fast-growing multi-billion dollar 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 Supply Quality organization has the charter to solve optimization problems for ad-programs in Amazon and ensure high-quality ad-impressions. We develop advanced algorithms and infrastructure systems to optimize performance for our advertisers and publishers. We are focused on solving a wide variety of problems in computational advertising like traffic quality prediction (robot and fraud detection), Security forensics and research, Viewability prediction, Brand Safety, Contextual data processing and classification. Our team includes experts in the areas of distributed computing, machine learning, statistics, optimization, text mining, information theory and big data systems. We are looking for a dynamic, innovative and accomplished Data Scientist to work on data science initiatives for contextual data processing and classification that power our contextual advertising solutions. Are you an experienced user of sophisticated analytical techniques that can be applied to answer business questions and chart a sustainable vision? Are you exited by the prospect of communicating insights and recommendations to audiences of varying levels of technical sophistication? Above all, are you an innovator at heart and have a track record of resolving ambiguity to deliver result? As a data scientist, you help our data science team build cutting edge models and measurement solutions to power our contextual classification technology. As this is a new initiative, you will get an opportunity to act as a thought leader, work backwards from the customer needs, dive deep into data to understand the issues, define metrics, conceptualize and build algorithms and collaborate with multiple cross-functional teams. Key job responsibilities * Define a long-term science vision for contextual-classification tech, driven fundamentally from the needs of our advertisers and publishers, translating that direction into specific plans for the science team. Interpret complex and interrelated data points and anecdotes to build and communicate this vision. * Collaborate with software engineering teams to Identify and implement elegant statistical and machine learning solutions * Oversee the design, development, and implementation of production level code that handles billions of ad requests. Own the full development cycle: idea, design, prototype, impact assessment, A/B testing (including interpretation of results) and production deployment. * Promote the culture of experimentation and applied science at Amazon. * Demonstrated ability to meet deadlines while managing multiple projects. * Excellent communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles. We are open to hiring candidates to work out of one of the following locations: London, GBR
JP, 13, Tokyo
We are seeking a Principal Economist to be the science leader in Amazon's customer growth and engagement. The wide remit covers Prime, delivery experiences, loyalty program (Amazon Points), and marketing. We look forward to partnering with you to advance our innovation on customers’ behalf. Amazon has a trailblazing track record of working with Ph.D. economists in the tech industry and offers a unique environment for economists to thrive. As an economist at Amazon, you will apply the frontier of econometric and economic methods to Amazon’s terabytes of data and intriguing customer problems. Your expertise in building reduced-form or structural causal inference models is exemplary in Amazon. Your strategic thinking in designing mechanisms and products influences how Amazon evolves. In this role, you will build ground-breaking, state-of-the-art econometric models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities - Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. - Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. - Contribute to building a strong data science community in Amazon Asia. We are open to hiring candidates to work out of one of the following locations: Tokyo, 13, JPN
DE, BE, Berlin
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU | Berlin, DEU
DE, BY, Munich
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Munich, BE, DEU | Munich, BY, DEU | Munich, DEU
IT, MI, Milan
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Milan, MI, ITA