How Amazon Robotics researchers are solving a “beautiful problem”

Teaching robots to stow items presents a challenge so large it was previously considered impossible — until now.

The rate of innovation in machine learning is simply off the chart — what is possible today was barely on the drawing board even a handful of years ago. At Amazon, this has manifested in a robotic system that can not only identify potential space in a cluttered storage bin, but also sensitively manipulate that bin’s contents to create that space before successfully placing additional items inside — a result that, until recently, was impossible.

Related content
Why multimodal identification is a crucial step in automating item identification at Amazon scale.

This journey starts when a product arrives at an Amazon fulfillment center (FC). The first order of business is to make it available to customers by adding it to the FC's available inventory.

The stowing process

In practice, this means picking it up and stowing it in a storage pod. A pod is akin to a big bookcase, made of sturdy yellow fabric, that comprises up to 40 cubbies, known as bins. Each bin has strips of elastic across its front to keep the items inside from falling out. These pods are carried by a wheeled robot, or drive unit, to the workstation of the Amazon associate doing the stowing. When the pod is mostly full, it is wheeled back into the warehouse, where the items it contains await a customer order.

Stowing is a major component of Amazon’s operations. It is also a task that seemed an intractable problem from a robotic automation perspective, due to the subtlety of thought and dexterity required to do the job.

Picture the task. You have an item for stowing in your hand. You gauge its size and weight. You look at the array of bins before you, implicitly perceiving which are empty, which are already full, which bins have big chunks of space in them, and which have the potential to make space if you, say, pushed all the items currently in the bin to one side. You select a bin, move the elastic out of the way, make room for the item, and pop it in. Job done. Now repeat.

“Breaking all existing industrial robot thinking”

This stow task requires two high-level capabilities not generally found in robots. One, an excellent understanding of the three-dimensional world. Two, the ability to manipulate a wide range of packaged but sometimes fragile objects — from lightbulbs to toys — firmly, but sensitively: pushing items gently aside, flipping them up, slotting one item at an angle between other items and so on.

A simulation of robotic stowing

For a robotic system to stand a chance at this task, it would need intelligent visual perception, a free-moving robot arm, an end-of-arm manipulator unknown to engineering, and a keen sense of how much force it is exerting. In short: good luck with that.

“Stow fundamentally breaks all existing industrial robotic thinking,” says Siddhartha Srinivasa, director of Amazon Robotics AI. “Industrial manipulators are typically bulky arms that execute fixed trajectories very precisely. It’s very positional.”

When Srinivasa joined Amazon in 2018, multiple robotics programs had already attempted to stow to fabric pods using stiff positional manipulators.

“They failed miserably at it because it's a nightmare. It just doesn't work unless you have the right computational tool: you must not think physically, but computationally.”

Srinivasa knew the science for robotic stow didn’t exist yet, but he knew the right people to hire to develop it. He approached Parker Owan as he completed his PhD at the University of Washington.

A “beautiful problem”

Parker Owan, Robotics AI senior applied scientist, poses next to a robotic arm and in front of a yellow soft sided storage pod
Parker Owan, Robotics AI senior applied scientist

“At the time I was working on robotic contact, imitation learning, and force control,” says Owan, now a Robotics AI senior applied scientist. “Sidd said ‘Hey, there’s this beautiful problem at Amazon that you might be interested in taking a look at’, and he left it at that.”

The seed was planted. Owan joined Amazon, and then in 2019 dedicated himself to the stow challenge.

“I came at it from the perspective of decision-making algorithms: the perception needs; how to match items to the appropriate bin; how to leverage information of what's in the bin to make better decisions; motion planning for a robot arm moving through free space; and then actually making contact with products and creating space in bins.”

Aaron Parness, Robotics AI senior manager of applied science, poses near a robotic arm
Aaron Parness, Robotics AI senior manager of applied science

About six months into his exploratory work, Owan was joined by a small team of applied scientists, and hardware expert Aaron Parness, now a Robotics AI senior manager of applied science. Parness admits he was skeptical.

“My initial reaction was ‘Oh, how brave and naïve that this guy, fresh out of his PhD, thinks robots can deal with this level of clutter and physical contact!’”

But Parness was quickly hooked. “Once you see how the problem can be broken down and structured, it suddenly becomes clear that there's something super useful and interesting here.”

“Uncharted territory”

From a hardware perspective, the team needed to find a robot arm with force feedback. They tried several, before the team landed on an effective model. The arm provides feedback hundreds of times per second on how much force it is applying and any resistance it is meeting. Using this information to control the robot is called compliant manipulation.

“We knew from the beginning that we needed compliant manipulation, and we hadn't seen anybody in industry do this at scale before,” says Owan. “It was uncharted territory.”

Parness got to work on the all-important hardware. The problem of moving the elastics aside to stow an item was resolved using a relatively simple hooking system.

How the band separator works

The end-of-arm tool (EOAT) proved to be a next-level challenge. One reason that stowing is difficult for robots is the sheer diversity of items Amazon sells, and their associated packaging. You might have an unpumped soccer ball next to a book, next to a sports drink, next to a T-shirt, next to a jewelry box. A robot would need to handle this level of variety. The EOAT evolved quickly over two years, with multiple failures and iterations.

Paddles grip an array of items

“In the end, we found that gently squeezing an item between two paddles was the more stable way to hold items than using suction cups or mechanical pinchers,” says Parness.

However, the paddle set up presented a challenge when trying to insert held items into bins — the paddles kept getting in the way. Parness and his growing team hit upon an alternative: holding the item next to a bin, before simultaneously opening the paddles and using a plunger to push the item in. This drop-and-push technique was prone to errors because not all items reacted to it in the same way.

The EOAT’s next iteration saw the team put miniature conveyor belts on each paddle, enabling the EOAT to feed items smoothly into the bins without having to enter the bin itself.

The miniature conveyor belt works to bring an item to its designated bin

“With that change, our stowing success rate jumped from about 80% to 99%. That was a eureka moment for us — we knew we had our winner,” says Parness.

Making space with motion primitives

The ability to place items in bins is crucial, but so is making space in cluttered bins. To better understand what would be required of the robot system, the team closely studied how they performed the task themselves. Owan even donned a head camera to record his efforts.

The team was surprised to find that the vast majority of space-making hand movements within a fabric bin could be boiled down to four types or “motion primitives”. These include a sideways sweep of the bin’s current contents, flipping upright things that are lying flat, stacking, and slotting something at an angle into the gap between other items.

The process of making space

The engineers realized that the EOAT’s paddles could not get involved with this bin-manipulation task, because they would get in the way. The solution, in the end, was surprisingly simple: a thin metal sheet that could extend from the EOAT, dubbed “the spatula”. The extended spatula can firmly, but sensitively, push items to one side, flip them up, and generally be used to make room in a bin, before the paddles eject an item into the space created.

But how does the system know how full the pod’s bins are, and how does it decide where, and how, it will make space for the next item to be stowed? This is where visual perception and machine learning come into play.

Deciding where to attempt to stow an item requires a good understanding of how much space, in total, is available in each fabric bin. In an ideal world, this is where 3D sensor technologies such as LiDAR would be used. However, because the elastic cords across the front of every bin partially blocks the view inside, this option isn’t feasible.

A robot arm executes motion primitives

Instead, the system’s visual perception is based on cameras pointed at the pod that feed their image data to a machine learning system. Based on what it can see of each bin’s contents, the system “erases” the elastics and models what is lying unseen in the bin, and then estimates the total available space in each of the pod’s bins.

Often there is space available in a cluttered bin, but it is not contiguous: there are pockets of space here and there. The ML system — based in part on existing models developed by the Amazon Fulfillment Technologies team — then predicts how much contiguous space it can create in each bin, given the motion primitives at its disposal.

How the perception system "sees" available space

“These primitives, each of which can be varied as needed, can be chained in infinitely many ways,” Srinivasa explains. “It can, say, flip it over here, then push it across and drop the item in. Humans are great at identifying these primitives in the first place, and machine learning is great at organizing and orchestrating them.”

When the system has a firm idea of the options, it considers the items in its buffer — an area near the robot arm’s gantry in which products of various shapes and sizes wait to be stowed — and decides which items are best placed in which bins for maximum efficiency.

“For every potential stow, the system will predict its likelihood of success,” says Parness. “When the best prediction of success falls to about 96%, which happens when a pod is nearly full, we send that pod off and wheel in a new one.”

“Robots and people work together”

At the end of summer 2021, with its potential feasibility and value becoming clearer, the senior leadership team at Amazon gave the project their full backing.

“They said ‘As fast as you can go; whatever you need’. So this year has been a wild, wild ride. It feels like we’re a start-up within Amazon,” says Parness, who noted the approach has significant advantages for FC employees as well.

Related content
Amazon fulfillment centers use thousands of mobile robots. To keep products moving, Amazon Robotics researchers have crafted unique solutions.

“Robots and people work together in a hybrid system. Robots handle repetitive tasks and easily reach to the high and low shelves. Humans handle more complex items that require intuition and dexterity. The net effect will be more efficient operations that are also safer for our workers.”

Prototypes of the robotic stow workstation are installed at a lab in Seattle, Washington, and another system has been installed at an FC in Sumner, Washington, where it deals with live inventory. Already, the prototypes are stowing items well and showcasing the viability of the system.

“And there are always four or five scientists and engineers hovering around the robot, documenting issues and looking for improvements,” says Parness.

Stow will be the first brownfield automation project, at scale, at Amazon. We're enacting a future in which robots and humans can actually work side by side without us having to dramatically change the human working environment.
Siddhartha Srinivasa

This year, in a stowing test designed to include a variety of challenging product attributes — bagged items, irregular items with an offset center of gravity, and so on — the system successfully stowed 94 of 95 items. Of course, some items can never be stowed by this system, including particularly bulky or heavy products, or cylindrical items that don’t behave themselves on conveyor belts. The team’s ultimate target is to be able to stow 85% of products stocked by a standard Amazon FC.

“Interacting with chaotic arrangements of items, unknown items with different shapes and sizes, and learning to manipulate them in intelligent ways, all at Amazon scale — this is ground-breaking,” says Owan. “I feel like I’m at ground zero for a big thing, and that’s what makes me excited to come to work every day.”

“Stow will be the first brownfield automation project, at scale, at Amazon,” says Srinivasa. “Surgically inserting automation into existing buildings is very challenging, but we're enacting a future in which robots and humans can actually work side by side without us having to dramatically change the human working environment.

Related content
Company is testing a new class of robots that use artificial intelligence and computer vision to move freely throughout facilities.

"One of the advantages of the type of brownfield automation we do at Robotics AI is that it’s minimally disruptive to the process flow or the building space, which means that our robots can truly work alongside humans," Srinivasa adds. "This is also a future benefit of compliant arms as they can, via software and AI, be made safer than industrial arms.”

Robots and humans working side by side is key to the long-term expansion of this technology beyond retail, says Parness.

“Think of robots loading delicate groceries or, longer term, loading dishwashers or helping people with tasks around the house. Robots with a sense of force in their control loop is a new paradigm in compliant-robotics applications.”

Research areas

Related content

US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians on a mission to develop a fault-tolerant quantum computer. You will be joining a team located in Pasadena, CA that conducts materials research to improve the performance of quantum processors. We are looking to hire a Quantum Research Scientist who will apply their expertise in materials characterization to the optimization of fabricated superconducting quantum devices. In this role, you are expected to lead and assist research projects that are aligned with our Center’s technical roadmap. You will develop new ideas and design experiments aimed at identifying the most promising material systems, characterization techniques, and integration processes for superconducting circuit applications. Key job responsibilities - Conduct experimental studies on the fundamental properties of superconducting, semiconducting, and dielectric thin films - Develop and implement multi-technique materials characterization workflows for thin films and devices, with a focus on the surfaces and interfaces - Work closely with other research scientists on the Materials team to develop material processes directed toward optimizing thin film properties, controlling the surface chemistry and morphology, and impacting device performance - Identify materials properties (chemical, structural, electronic, electrical) that can be a reliable proxy for the performance of superconducting qubits and microwave resonators - Communicate engineering and scientific findings to teammates, the broader CQC and, when appropriate, publish findings in scientific journals A day in the life AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. About the team Our team contributes to the fabrication of processors and other hardware that enable quantum computing technologies. Doing that necessitates the development of materials with tailored properties for superconducting circuits. Research Scientists and Engineers on the Materials team operate deposition and characterization systems in order to develop and optimize thin film processes for use in these devices. They work alongside other Research Scientists and Engineers to help deliver fabricated devices for quantum computing experiments. We are open to hiring candidates to work out of one of the following locations: Pasadena, CA, USA
US, CA, Sunnyvale
Help re-invent how millions of people watch TV! Fire TV remains the #1 best-selling streaming media player in the US. Our goal is to be the global leader in delivering entertainment inside and outside the home, with the broadest selection of content, devices and experiences for customers. Our science team works at the intersection of Recommender Systems, Information Retrieval, Machine Learning and Natural Language Understanding. We leverage techniques from all these fields to create novel algorithms that allow our customers to engage with the right content at the right time. Our work directly contributes to making our devices delightful to use and indispensable for the household. Key job responsibilities - Drive new initiatives applying Machine Learning techniques to improve our recommendation, search and entity matching algorithms - Perform hands-on data analysis and modeling with large data sets to develop insights that increase device usage and customer experience - Design and run A/B experiments, evaluate the impact of your optimizations and communicate your results to various business stakeholders - Work closely with product managers and software engineers to design experiments and implement end-to-end solutions - Setup and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them - Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences - Help attract and recruit technical talent; mentor junior scientists We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
SG, Singapore
Do you want to contribute to a team working on cutting edge technology, solve new problems that didn’t exist before, and have the ability to see the impact of your successes? Amazon is shaping the future of digital video entertainment. We seek experienced data scientists who can apply the latest research, state-of-the-art algorithms and machine learning to solve core problems in the video streaming space for Amazon. This is an exciting opportunity for candidates with a deep understanding of large data sets and structures, customer behavior and signals, machine learning algorithms and production pipelines. If you are passionate about solving complex problems in a challenging environment, we would love to talk with you. We are looking for a seasoned data scientist who can help us scale our video streaming and advertising business. He/She will develop and build machine learning models using large data-sets to improve our customer and advertiser experience, and will work closely with technology teams in deploying the models to production. He/She will work in a highly collaborative environment with some of the best engineers, marketers and product managers, and be part of a rapidly growing initiative which is going to become a huge area of growth for Amazon's Advertising business and pioneer the usage of new technology at Amazon scale. Key job responsibilities - Engage in advanced data analysis to uncover trends and correlations. Utilize statistical methods and tools to drive insightful recommendations for business strategies and process improvements. - Use the data insights to design, develop and build scalable and advanced machine learning models, algorithms and implement them in production through robust systems and architectures - Work closely with stakeholders across various departments including product, business analytics, marketing, operations and tech teams and influence business strategy - Be abreast of the advanced research and techniques in the deep-learning and artificial intelligence space, and conduct experiments to give the best output - Identify, develop, manage, and execute data analyses to uncover areas of opportunity and present business recommendations to drive cost benefit analysis and go/no-go decisions on various initiatives - Develop a roadmap and metrics to measure progress of the initiative they own - Lead initiatives for full-scale automation in collaboration with data engineering teams, enhancing data accuracy and operational efficiency We are open to hiring candidates to work out of one of the following locations: Singapore, SGP
SG, Singapore
Do you want to contribute to a team working on cutting edge technology, solve new problems that didn’t exist before, and have the ability to see the impact of your successes? Amazon is shaping the future of digital video entertainment. We seek experienced data scientists who can apply the latest research, state-of-the-art algorithms and machine learning to solve core problems in the video streaming space for Amazon. This is an exciting opportunity for candidates with a deep understanding of large data sets and structures, customer behavior and signals, machine learning algorithms and production pipelines. If you are passionate about solving complex problems in a challenging environment, we would love to talk with you. We are looking for a seasoned data scientist who can help us scale our video streaming and advertising business. He/She will develop and build machine learning models using large data-sets to improve our customer and advertiser experience, and will work closely with technology teams in deploying the models to production. He/She will work in a highly collaborative environment with some of the best engineers, marketers and product managers, and be part of a rapidly growing initiative which is going to become a huge area of growth for Amazon's Advertising business and pioneer the usage of new technology at Amazon scale. Key job responsibilities - Engage in advanced data analysis to uncover trends and correlations. Utilize statistical methods and tools to drive insightful recommendations for business strategies and process improvements. - Use the data insights to design, develop and build scalable and advanced machine learning models, algorithms and implement them in production through robust systems and architectures - Work closely with stakeholders across various departments including product, business analytics, marketing, operations and tech teams and influence business strategy - Be abreast of the advanced research and techniques in the deep-learning and artificial intelligence space, and conduct experiments to give the best output - Identify, develop, manage, and execute data analyses to uncover areas of opportunity and present business recommendations to drive cost benefit analysis and go/no-go decisions on various initiatives - Develop a roadmap and metrics to measure progress of the initiative they own - Lead initiatives for full-scale automation in collaboration with data engineering teams, enhancing data accuracy and operational efficiency We are open to hiring candidates to work out of one of the following locations: Singapore, SGP
SG, Singapore
Do you want to contribute to a team working on cutting edge technology, solve new problems that didn’t exist before, and have the ability to see the impact of your successes? Amazon is shaping the future of digital video entertainment. We seek experienced data scientists who can apply the latest research, state-of-the-art algorithms and machine learning to solve core problems in the video streaming space for Amazon. This is an exciting opportunity for candidates with a deep understanding of large data sets and structures, customer behavior and signals, machine learning algorithms and production pipelines. If you are passionate about solving complex problems in a challenging environment, we would love to talk with you. We are looking for a seasoned data scientist who can help us scale our video streaming and advertising business. He/She will develop and build machine learning models using large data-sets to improve our customer and advertiser experience, and will work closely with technology teams in deploying the models to production. He/She will work in a highly collaborative environment with some of the best engineers, marketers and product managers, and be part of a rapidly growing initiative which is going to become a huge area of growth for Amazon's Advertising business and pioneer the usage of new technology at Amazon scale. Key job responsibilities - Engage in advanced data analysis to uncover trends and correlations. Utilize statistical methods and tools to drive insightful recommendations for business strategies and process improvements. - Use the data insights to design, develop and build scalable and advanced machine learning models, algorithms and implement them in production through robust systems and architectures - Work closely with stakeholders across various departments including product, business analytics, marketing, operations and tech teams and influence business strategy - Be abreast of the advanced research and techniques in the deep-learning and artificial intelligence space, and conduct experiments to give the best output - Identify, develop, manage, and execute data analyses to uncover areas of opportunity and present business recommendations to drive cost benefit analysis and go/no-go decisions on various initiatives - Develop a roadmap and metrics to measure progress of the initiative they own - Lead initiatives for full-scale automation in collaboration with data engineering teams, enhancing data accuracy and operational efficiency We are open to hiring candidates to work out of one of the following locations: Singapore, SGP
US, WA, Seattle
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you fascinated by the use of Generative AI to build an advertiser facing solution that predict problems and coach users while they solve real word problems? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the customer service space? If so, Amazon's Support Product & Services (SP&S) team has an exciting opportunity for you as an Applied Scientist. Key job responsibilities • Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the advertising support center domain. • Use Transformers and apply other NLP techniques like Sentence embeddings, Dimensionality reduction, clustering and topic modeling to identify customer intents and utterances. • Use services like AWS Lex, AWS Bedrock etc. to develop advertising facing solutions • Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful solutions. • Automating feedback loops for algorithms in production. • Setup and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them. • Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences. A day in the life You will work closely with a cross functional team of Software Engineers, Product Owners, Data Scientists, and Contact Center experts. You will research and investigate the latest options in industry to apply machine learning and generative AI to real world problems. You will work backwards from customer problems and collaborate with stakeholders to determine how to scale new technology and integrate with complicated help channels used by advertisers everyday. About the team SP&S team provides solutions and libraries that are leveraged by teams all across Amazon Advertising to provide timely and personalized help. The team aims to predict Advertisers problems and proactively surface intelligent guidance to customers at the right time. As a AS, you will help the team to achieve its vision of building and implementing the next generation of Contact Center technology. You will build/leverage LLMs to train them on advertising support domain knowledge and work shoulder to shoulder with stakeholders to externalize to users in novel ways. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Come be a part of a rapidly expanding $35 billion dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. We are seeking a Senior Applied Scientist who has a solid background in applied Machine Learning and Data Science, deep passion for building data-driven products, ability to formulate data insights and scientific vision, and has a proven track record of executing complex projects and delivering business impact. Key job responsibilities • Data driven insights to accelerate acquisition of new members. • Grow benefits adoption based on customer segment, vertical, and drive customers to their "aha moment". • Work closely with software engineering teams to drive model implementations and new feature creations. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. • Mentor junior scientists, provide technical and career development guidance. About the team The Marketing Science team applies scientific methods and research techniques to enhance our understanding of AB consumer behavior, market trends, and the effectiveness of marketing strategies. Our goal is to develop and advance theories and models that can be used to make informed decisions in marketing and to provide insights into consumer decision-making processes. Additionally, we seek to identify and explore emerging trends and technologies in marketing, and to develop innovative approaches for addressing the challenges and opportunities in the field. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Amazon brings buyers and sellers together. Our retail customers depend on us to give them access to every product at the best possible price. Our sellers depend on us to give them a platform to launch their business into every home and marketplace. Making this happen is the mission of every scientist in North America Stores (NAS) organization. To this end, the Science team is tasked with: · Building and deploying AI / ML models that lead to exponential growth of the business. · Organizing available data sources, and creating detailed dictionaries of data that can be used in future analyses. · Partnering with product teams in evaluating the financial and operational impact of new product offerings. · Partnering with science teams across other organizations to develop state of the art algorithms and models. · Carrying out independent data-backed initiatives that can be leveraged later on in the fields of network organization, costing and financial modeling of processes. · Publishing papers in both internal and external conferences / journals. In order to execute the above mandate we are on the look out for smart and qualified Applied Scientists who will own projects in partnership with product and research teams as well as operate autonomously on independent initiatives that are expected to unlock benefits in the future. A past background in Artificial Intelligence is necessary, along with advanced proficiency in programming languages such as Python and C++. Key job responsibilities As an Applied Scientist, you are able to use a range of artificial intelligence and operations research methodologies to solve challenging business problems when the solution is unclear. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as Redshift, Sagemaker, Lambda, S3, and EC2 with a variety of skillsets in Tabular ML, NLP, Generative AI, Forecasting, Probabilistic ML and Causal ML. You will bring knowledge in many of these domains along with your own specialties and skill-sets. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Seattle, WA, USA
US, NY, New York
Amazon is looking for a Senior Applied Scientist to help build the next generation of sourcing and vendor experience systems. The Optimal Sourcing Systems (OSS) owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units. Our goals are to increase access to supply for speed and placement, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon. As a Senior Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. Some critical research areas in our space include modeling buying decisions under high uncertainty, vendors' behavior and incentives, supply risk and enhancing visibility and reliability of inbound signals. Key job responsibilities You will be a science tech leader for the team. As a Senior Applied Scientist you will: - Lead a team of scientists to innovate on state-of-the-art sourcing systems. - Set the scientific strategic vision for the team. You lead the decomposition of problems and development of roadmaps to execute on it. - Set an example for other scientists with exemplary scientific analyses; maintainable, extensible, and well-tested code; and simple, intuitive, and effective solutions. - Influence team business and engineering strategies. - Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs. - Communicate clearly and effectively with stakeholders to drive alignment and build consensus on key initiatives. - Foster collaborations between scientists across Amazon researching similar or related problems. - Actively engage in the development of others, both within and outside the team. - Engage with the broader scientific community through presentations, publications, and patents. To help describe some of our challenges, we created a short video about SCOT at Amazon: http://bit.ly/amazon-scot About the team Supply Chain Optimization Technologies (SCOT) owns Amazon's global inventory planning systems. We decide what, when, where, and how much we should buy to meet Amazon's business goals and to make our customers happy. We decide how to place and move inventory within Amazon's fulfillment network. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars worldwide. Check our website if you are curious to learn more about the breadth of problems we tackle: https://www.amazon.science/tag/supply-chain-optimization-technologies We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | New York, NY, USA
US, WA, Seattle
We’re building a foundation LLM for Amazon Stores that fuses general world knowledge with Amazon e-commerce domain knowledge to provide new and improved shopping experiences for our customers. We are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You’ll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA