History of SCOT lead image.jpg
In a little over a decade, Amazon’s Supply Chain Optimization Technologies team (SCOT) has built one of the largest and most sophisticated automated decision-making systems in the world.

Solving some of the largest, most complex operations problems

How Amazon’s Supply Chain Optimization Technologies team has evolved over time to meet a challenge of staggering complexity.

Amazon’s ability to grow to an unprecedented scale, while simultaneously meeting the growing expectations of its customers, particularly around delivery speeds, is a success story on many levels.

One of the keys to that success is a team that is fundamental to Amazon’s increasingly rapid transformation. A largely unsung team that in little more than a decade has built one of the largest and most sophisticated automated decision-making systems in the world. A team that has harnessed simulation, mathematical optimization, and machine learning to create the capability to deliver products at speeds once thought impossible at the mass market scale — in some cases within 2 hours — across a fulfillment network of dizzying complexity.

This is Amazon’s Supply Chain Optimization Technologies team (SCOT). If the Amazon Store were a human body, think of SCOT as its nervous system: essential to life, quietly acting in the background to automatically optimize critical functions and flows.

“At SCOT, using science and technology to optimize the supply chain is not just an enabler, it's our core focus,” says Ashish Agiwal, vice president, Fulfillment Optimization.

Today, SCOT’s systems have end-to-end responsibility for orchestrating Amazon Store’s supply chain.

SCOT is responsible for computing the delivery promises Amazon Store customers see when ordering, forecasting demand for its hundreds of millions of products, deciding which products to stock and in what quantities, allocating stock to warehouses and fulfillment centers (FCs) in anticipation of regional customer needs, offering markdown pricing when necessary, working out how to consolidate customer orders for maximum efficiency, coordinating inbound and inventory management from millions of sellers worldwide, and so much more.

But it was not always thus. Far from it, says Deepak Bhatia, vice president of SCOT, whose team’s methodologies and mechanisms will be a topic of conversation at INFORMS, the world’s largest operations research and analytics conference, taking place next week in Indianapolis, Indiana.

“A very different world”

In 2011 when Bhatia joined Amazon, the team that would evolve into SCOT was much smaller, he recalls, and its main concern was trying to automate Amazon’s product buying and inventory management.

“It was a very different world. The notion of an end-to-end supply chain tech function wasn’t there. But there were powerful intellects and a lot of energy in that team.”

It was a huge deal. Will it improve things, and if so by how much? Will it completely break? In the beginning, we took baby steps. We made changes one product category at a time.
Deepak Bhatia

In 2011, Amazon’s total revenue reached nearly $48 billion, and it was already clear to the senior leadership that the company’s scale would require the automation of buying and the management of inventory; monitoring spreadsheets was not a long-term solution. Indeed, even then the sheer range of products offered by Amazon meant the “illusion of control” was already kicking in among the groups managing inventory, says Bhatia. In fact, Bhatia notes, the sheer complexity and scale meant the challenge was beyond the scope of any team, let alone an individual.

In response, Bhatia and his colleagues set out to develop complex algorithms that could make buying and inventory placement decisions for a given category of products. And while that was all well and good in theory, trying it for real was a watershed moment.

“It was a huge deal. Will it improve things, and if so by how much? Will it completely break? In the beginning, we took baby steps. We made changes one product category at a time.”

Media category products were the early adopters. In randomized, controlled trials that ran over several months, some of these products were managed in the traditional way, and some by the new algorithms. Crucially, human judgement could still override the system’s decisions if deemed necessary.

The trial went well — the algorithms’ decisions were overridden only a small percentage of the time — and the approach was expanded across additional categories, including consumables such as groceries.

Going all in

“Then one day, in a high-level meeting someone said: ‘What if we go all in and make these categories 100% automated?’, Bhatia recalls. “Someone responded ‘All hell will break loose’.” And that, Bhatia notes, is where Amazon’s comfort with risk-taking came into play. “They decided to go all in.” That was around 2014. And the systems worked as designed, improving customer experience outcomes like in-stock rates while reducing costs.

One day, in a high-level meeting someone said: ‘What if we go all in and make these categories 100% automated?’ Someone responded ‘All hell will break loose’.
Deepak Bhatia

“After this success, automating one product category at a time started to feel too risk-averse,” says Bhatia.

Over the next few years, the technology was rapidly rolled out across the retail business, all the while being iterated and improved upon, with increasing success in terms of efficiency and customer satisfaction. At the same time, the rapidly growing SCOT team was developing technologies that would enable them to join the dots from one end of the Amazon supply chain to the other.

For example, SCOT grew its own demand forecasting team, with a sharp focus on scientific and technological innovation. The forecasting aspect of SCOT’s work started out as a patchwork of models, which evolved eventually to deep learning approaches to decide what features of the retail data were most important.

Related content
The story of a decade-plus long journey toward a unified forecasting model.

Today, building on a 2018 in-house research breakthrough, the forecasting team is using a single model that learns business-critical demand patterns without even being told what to look for. Called the Multi-Horizon Quantile Recurrent Forecaster, the model can accurately forecast shifting seasonal demand, future planned-event demand spikes and even “cold-start forecasting” for products with limited sales history.

Forecast accuracy is particularly important at Amazon’s scale.

“SCOT is directing hundreds of billions of dollars of product flows. That means just a few percentage points of change in our topline predictions equates to several fulfillment centers worth of products,” says Salal Humair, a SCOT vice president and Amazon distinguished scientist.

As SCOT’s demand forecasting has improved, so too has its ability to ensure that products were best positioned to fulfill those anticipated customer orders.

The challenge of One-Day Delivery

While Amazon’s largely manual inventory management system became increasingly automated in the early part of the previous decade, those changes proved insufficient for the logistical challenges that lay ahead: Amazon’s ever more ambitious customer-delivery promises, particularly its One-Day Delivery promise in the US in 2019, and Prime Now, Amazon's 2-hour grocery businesses.

“Before we announced the One-Day Delivery promise, a detailed SCOT simulation called Mechanical Sensei was the key to figuring out how much additional inventory we would need, where it would be placed, and how that would affect shipping costs,” says Humair.

So, at a time when Amazon was continuing to expand globally, the company’s bold delivery promises meant there was a pressing need to locate products closer to Amazon customers. This meant a significant increase in local distribution facilities, and yet another challenge: which items should be locally placed?

“Most of our systems were designed to operate under the simplifying assumption that demand for each item sold on the website is independent, but we know that’s not the case in reality,” says Jeffrey Maurer, vice president, Inventory Planning and Control. “When one product goes out of stock, or isn’t available for fast delivery, demand shifts to other products. We can’t make every product locally available in every location, so how do we account for these constraints while trying to maximize customer satisfaction?”

That nut has yet to be comprehensively cracked, but the simple fact of adding local warehousing resulted in a supply chain network of such layered complexity, that the SCOT team realized its automated network would need yet another radical redesign.

From left to right, Ashish Agiwal, vice president, Fulfillment Optimization; Deepak Bhatia, vice president of SCOT; Salal Humair, a SCOT vice president and Amazon distinguished scientist; Jeffrey Maurer, vice president, Inventory Planning and Control; and Piyush Saraogi, vice president, Fulfillment By Amazon.
From left to right, Ashish Agiwal, vice president, Fulfillment Optimization; Deepak Bhatia, vice president of SCOT; Salal Humair, a SCOT vice president and Amazon distinguished scientist; Jeffrey Maurer, vice president, Inventory Planning and Control; and Piyush Saraogi, vice president, Fulfillment By Amazon.

It took them several years to solve for the new set of challenges.

“We had to iterate, fail, iterate, fail, iterate, fail many times,” Humair recalls.

Then, in 2020, the team unveiled its latest breakthrough: the “multi-echelon system”. This is a multi-product, multi-layered, multi-fulfillment center model for optimizing inventory levels for varying delivery speeds in a space where future demand, product lead times and capacity constraints are all uncertain, and where real-time customer promises and fulfillment make the demand patterns seen by FCs very hard to characterize.

“We have a strong sense of pride for the work the SCOT team is doing,” says Bhatia. “These sorts of solutions are just unheard of in academia and industry.”

The SCOT team was able to demonstrate significant improvements to inventory buying and placement through the multi-echelon system, but rolling it out across the business was a challenge.

“Not only did the teams, systems and coordination mechanisms all need to be rebuilt, but we also had to keep the business running,” says Humair. “We had to change the engine while still flying the plane!”

Related content
The SCOT science team used lessons from the past — and improved existing tools — to contend with “a peak that lasted two years”.

And then there was COVID. “The impact of COVID on our supply chain brought capacity management to the forefront,” says Maurer. “It was no longer enough to be approximately right at network level in terms of capacity management; we needed to get it exactly right at every facility and connection in our network.”

Ultimately, the successful combination of powerful forecasting, multi-echelon inventory management‚ and several other algorithms and systems — running the gamut from fulfillment to customer promise, inventory health, and inventory placement — along with unparalleled distribution capacity enabled Amazon to deal with the effects of COVID as well as the enormous surges in demand created by shopping events such as Cyber Monday and Amazon’s own Prime Day. The latter, this year, resulted in the record-breaking purchase of more than 300 million items across more than 20 countries.

Future challenges

So what are the current and future challenges in SCOT’s sights?

“The range of problems requiring disruptive technology solutions is not exhausted,” Humair notes.

For example, about 60% of the Amazon Store’s sales is through Fulfillment by Amazon (FBA), a service for small-and-medium sized businesses to provide unique selection for Amazon customers at low costs and fast speeds.

Optimizing supply chain efficiency would be hard enough at Amazon’s scale, even if Amazon was in full control of every aspect of its fulfillment network. “However we work with millions of FBA sellers with different cost structures and inventory management practices who independently decide what to sell, how much to inbound, and how to price their products,” notes Piyush Saraogi, vice president, FBA.

Related content
INFORMS talk explores techniques Amazon’s Supply Chain Optimization Technologies organization is testing to fulfill customer orders more efficiently.

These businesses share Amazon’s storage capacity and transportation network, but make their own decisions on pricing and inventory management. COVID played a role here as well: capacity constraints meant the FBA team had to adopt limits on restocking.

“Balancing the supply and demand of capacity in a network with 60% FBA inventory is an incredibly complex business problem,” Saraogi says. “To balance capacity in the marketplace setting, we have to invent new approaches that offer predictability to our sellers and are consistent with our general laissez-faire approach to FBA, while giving Amazon the flexibility to balance the network and ensure our store has all the in-stock selection customers are looking for.

Sellers may have developed a blockbuster new product, received fresh capital, or shifted distribution toward FBA. The science for leveraging this key seller input in a scalable manner into our inventory and capacity management systems is an unchartered territory that our scientists, engineers, and product managers are working on.”

“This is a big challenge for SCOT,” Bhatia agrees. “How can we support all our independent third-party sellers in ways that result in a triple win, for them, for Amazon, and for our customers?”

The SCOT team also wrestles with something that is increasingly prevalent in the modern world of complex optimization modelling and machine learning: how to explain automated decisions to the people who need to understand why things are happening as they are.

“We have hundreds of people fielding questions from selling partners and other stakeholders,” says Humair. “Why have my in-stock rates changed? Why do I have more inventory? Each such question requires manual deep dives, hundreds of person hours to answer.” The team is currently developing new methods to make its systems more explainable.

These systems optimize millions of customer promises every second and billions of customer order fulfillment plans daily. This is done by evaluating hundreds of millions of potential transport routes across the network and tracking over a billion real-time inventory updates every day
Ashish Agiwal

Indeed, the very fact that such technology is extremely complex and requires a sophisticated technical background to fully understand makes the idea of going all-in on data science a daunting proposition,” says Humair.

“Data is always ambiguous, so you need a lot of conviction and judgment to stay the course. But it has yielded spectacular benefits for Amazon, for our selling partners, and, most importantly, for our customers.”

Another big challenge is managing transportation through Amazon’s growing delivery fleet of trucks, planes, sort centers, and delivery stations. SCOT’s Fulfillment Optimization team, led by Agiwal, runs the systems that makes outbound fulfillment decisions.

“These systems optimize millions of customer promises every second and billions of customer order fulfillment plans daily. This is done by evaluating hundreds of millions of potential transport routes across the network and tracking over a billion real-time inventory updates every day,” he says.

Amazon’s operation of its own transportation network has created what Agiwal calls “a very exciting problem space” that his team is now addressing. “Designing the network topology, optimizing connections in a multi-tier multi-modal network, and coordinating all operational resources at Amazon scale is unprecedented,” he notes.

“Our new priority is ensuring that our own delivery trucks or cargo planes are as full as possible while also meeting our customer-delivery windows,” says Bhatia.

That problem space also illustrates why Amazon SCOT is so unique.

“We are solving some of the largest, most complex problems in operations using solutions entirely built in-house,” says Agiwal. “We have some of the best scientists, engineers and product managers in the world, working together and controlling their own destiny. We have the luxury of large and diverse data sets and the ability to innovate and experiment at a massive scale with immediate, measurable impact on customer experience and costs. It is truly gratifying.”

That complexity also explains why SCOT is so appealing to data scientists, economists, and machine learning scientists of all stripes.

“Our problem dimensionality is high and closed-form solutions are rarely applicable,” notes Maurer. “Our teams continually invent and implement new algorithms and evolve the fundamental structure of our systems as the physical network changes. SCOT is a great place for people who are drawn to exceptionally complex problem spaces and motivated by having high production impact.”

Related content

GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. 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 lead the design of machine learning models 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 a science team 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 job responsibilities Develop machine learning algorithms for high-scale recommendations problems. 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 engineers 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.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are 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 to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers 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
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
IL, Tel Aviv
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, 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 that 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. 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. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience 3+ years of building models for business application experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
IL, Haifa
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, 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 that 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. 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. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.
US, NY, New York
Join us in a historic endeavor to make Generative AI accessible to the world with breakthrough research! The AWS AI 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 drives the innovation that enables external and internal SageMaker customers to train their next generation models on both GPU and Trainium instances. 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. About the team 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. Utility Computing (UC) 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. 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. 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. Mentorship and 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. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.