Animation shows a map of the United States and each of the 8 individual regions that resulted from Amazon's regionalization effort
Amazon's regionalization plan, which resulted in the eight regions seen here, has already proven successful: The percentage of customer orders being fulfilled entirely from FCs within each region has jumped to 76% — and is expected to continue to climb.

Sizing down to scale up: How Amazon reworked its fulfillment network to meet customer demand

The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.

In 2020, Amazon’s retail fulfillment network in the U.S. expanded at a rapid clip. What followed was a dramatic — and swift — operational pivot.

This is the story of how Amazon’s national network of U.S. fulfillment centers (FCs), intermediate sorting centers, “last mile” delivery hubs, and transportation fleet were successfully restructured into eight largely self-sufficient regional networks, while retaining national coverage. The transformation was dubbed “regionalization.”

Operations research at Amazon
How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.

The COVID pandemic was a key factor in two ways. Due to lockdowns or otherwise, people were staying home and ordering more online than ever before.

“Our focus moved from trying to figure out how to make customer deliveries as fast as possible to trying to meet exceptional customer demand by pushing as much volume as we could through our network,” says Adam Baker, Amazon’s vice president of global transportation.

It was in late 2020 that a long-term planning science team led by research director Amitabh Sinha sent up a warning flare: the fast-growing network risked becoming overcomplicated and unwieldy.

“We projected our scenario out to three or four years and took this to Amazon’s leadership with an idea of how to do things differently,” he says. That idea contained the seed that would grow into regionalization.

Joining the dots

The crux of the issue was that Amazon was trying to connect too many physical dots. Its fulfillment infrastructure made sense when it had fewer FCs, because it meant customers across the U.S. could tap into Amazon’s full product range. And with fewer FCs, the trucks carrying the products across the country were fuller, so it was cost-effective.

As the number of FCs and other fulfillment buildings in the U.S. rose sharply, that approach started to look like it might not be the right long-term path. “We would fulfill customer orders from the FCs near them until we couldn’t anymore, and then — okay, it's coming from wherever we still have capacity,” says Russell Allgor, Amazon’s chief scientist for worldwide operations. That “wherever” was the problem.

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

It meant each of Amazon’s FCs was serving not only its locality, but also customer locations all over the U.S. To illustrate the problem, imagine you had to deliver 10,000 products nationwide, quickly, to 100 distant locations, from 10 FCs across the country. You could have each FC dispatch 100 trucks, each carrying 10 items, to each of the locations. That’s 1,000 long-haul trucks and a lot of rubber on the road — clearly an unsustainable idea on all fronts.

Now imagine that you could partition the 100 customer locations into 10 regions of 10 locations apiece, with each region served by a dedicated FC. In this scenario, each region’s FC can dispatch 10 trucks, each carrying 100 packages a piece. That would require just 100 trucks nationwide, driving much shorter distances. That’s faster for customers and more sustainable: a win-win situation. That’s regionalization in a nutshell, and by mid-2021, Amazon threw its full weight behind the idea.

Picking the number

For about a year, Sinha and his team used state-of-the-art network-optimization tools to model and simulate the many potential ways customer orders might flow through a regionalized system, and what effect different configurations would have on delivery speeds and transport costs. There was an enormous number of potential scenarios to explore.

Operations research at Amazon
How the Amazon Logistics Research Science team guides important decisions related to last-mile delivery.

“We were dealing with millions of variables and constraints, and a lot of uncertainty,” says Cristiana Lara, a senior research scientist who worked on estimating the financial impacts of the initiative. “That’s not surprising, because we were completely shifting the paradigm of how we fulfill customer orders.”

A critical early question was how many regions to form. The smaller the regions, the faster the customer deliveries, because Amazon’s inventory would be closer to customers. “In addition to speedy deliveries, the crucial thing was that each region must carry the breadth of selection that customers expect” says Sinha.

The ambitious aim? To have a high proportion of the tens of millions of products offered in the store available to customers within each region, with the rest shipped from further afield only when needed.

Amazon's regionalization map, with 8 regions overlaid over a map of the United States, is seen here
A critical piece of regionalization was using the insights to map out more efficient, shorter routes for orders. As soon as a customer clicks the "buy now" button, Amazon's Adaptive TRansportation OPtimization Service (ATROPS) assigns the optimal route for the purchased item.

With this goal in mind, the collaborators alighted on the number: eight regions. That was as high as they could go without sacrificing speed or requiring excessive inter-regional movement of inventory to meet customer orders, which would defeat the purpose of the exercise.

“We ran extensive, fine-grained analysis for pretty much the entirety of 2022, examining in turn the different aspects of how it would all work,” says Sinha, whose team worked closely with Amazon’s Global Transportation Service (GTS) – which designs, plans, and executes the Amazon Transportation network.

Before long, a timeline was put in place. Come January 18, 2023, the newly minted Northeast and Mid-Atlantic Amazon regions would pioneer this new fulfillment pattern, with the other six regions slated to follow thereafter.

Flipping the switch

A critical piece of regionalization was using the insights supplied to map out more efficient, shorter routes for orders. As soon as a customer clicks the "buy now" button, Amazon's Adaptive TRansportation OPtimization Service (ATROPS) assigns the optimal route for the purchased item. The transportation team devoted the latter part of 2022 to overhauling and testing a completely new set of ATROPS routes designed specifically for this regionalization plan.

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

On January 18, with the 2022 holiday rush safely in the rearview mirror, it was time to make the leap. The transportation team had contingency plans in place, and colleagues in different global time zones were standing by to offer around-the-clock support if something went wrong.

“We flipped the switch overnight, and immediately started to see the results we were hoping for. It changed faster than any of us expected. It was delightful,” says Nick McCabe, senior manager of GTS network design.

“We had some minor concerns to work through,” Baker adds, “but our delivery speed instantly picked up and our customers saw the benefit in their orders right away.”

Overall, the transition went so well, Amazon brought forward the complete activation of the other regions by a full month.

Rapid results

Regionalization is working. Before the switch, the percentage of customer orders being fulfilled entirely from FCs within what would become each region was 62%. That figure has already surged to 76% — a stunning efficiency gain — and is expected to continue to climb.

Delivery speeds have also picked up, says Sinha, because more goods are travelling shorter distances. And this effect will only strengthen as regionalization continues to take root.

Another quick success of regionalization was how much fuller a subset of Amazon’s trucks has become. Most customer orders leave an FC and are transported to a sorting center, which receives and consolidates customer orders, filling up the trucks that take them to delivery stations for the “last mile” of their journey. Sometimes, for logistical reasons, FCs send trucks directly to delivery stations. Post-regionalization, because there are fewer FCs shipping more packages to each destination, there is greater opportunity to operate these FC-to-delivery station direct trucks, resulting in more efficient delivery routes.

“Suddenly, 70 to 80 per cent of the order volume is not coming from FCs scattered around the country, but from, say, 10 FCs inside the region, so trucks on these short-distance direct lanes are now showing a great fill rate,” says senior applied scientist Semih Atakan, who models how products flow between Amazon’s FCs and delivery stations.

Regionalization has also transformed how the wider national network is managed.

“Before, it was difficult to control the whole network because of our sheer number of trucking lanes,” says Baker. “It was like pushing on a giant spiderweb.” Post-regionalization, he says, that number of lanes reduced markedly, making it much easier to make choices about when and how much to ship between regions.

Scanning the horizon

And this is just the beginning, says research scientist Xiaoyan Si, who is modeling how the fulfillment network might evolve over the next three years.

“Eight regions is our starting point. As we move forward, we will have the opportunity create smaller geographic regions with as much demand per region as we have today,” says Si. “Using the data we have now, we can place future fulfillment buildings more strategically, and we are working with other researchers on the team to design new regions more scientifically.”

Smaller regions will enable Amazon to deliver even faster to customers, while making each region even more efficient in terms of distance travelled, inventory management and truck fill.

Amazon’s Day One culture places great value on horizon scanning, innovation, and risk-taking to deliver customer benefits. The regionalization initiative that sprang from this mindset is a testament not only to the vision and enormous team effort required to pull it off, but also to the flexibility of Amazon’s infrastructure.

Because despite being Amazon’s biggest operational transformation in a decade, it was completely reversible had it misfired. After all, says Si, in what might be the understatement of the year: “When you boil it right down, regionalization is just a software setting.”

Related content

IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead 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). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team 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. 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. 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. 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 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.
US, CA, Palo Alto
Amazon Sponsored Products is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of GenAI/LLM powered self-service performance advertising products that drive discovery and sales. Our products are strategically important to Amazon’s Selling Partners and key to driving their long-term growth. We deliver billions of ad impressions and clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. This role will be pivotal within the Autonomous Campaigns org of Sponsored Products Ads, where we're pioneering the development of AI-powered advertising innovations that will redefine the future of campaign management and optimization. As a Principal Applied Scientist, you will lead the charge in creating the next generation of self-operating, GenAI-driven advertising systems that will set a new standard for the industry. Our team is at the forefront of designing and implementing these transformative technologies, which will leverage advanced Large Language Models (LLMs) and sophisticated chain-of-thought reasoning to achieve true advertising autonomy. Your work will bring to life systems capable of deeply understanding the nuanced context of each product, market trends, and consumer behavior, making intelligent, real-time decisions that surpass human capabilities. By harnessing the power of these future-state GenAI systems, we will develop advertising solutions capable of autonomously selecting optimal keywords, dynamically adjusting bids based on complex market conditions, and optimizing product targeting across various Amazon platforms. Crucially, these systems will continuously analyze performance metrics and implement strategic pivots, all without requiring manual intervention from advertisers, allowing them to focus on their core business while our AI works tirelessly on their behalf. This is not simply about automating existing processes; your work will redefine what's possible in advertising. Our GenAI systems will employ multi-step reasoning, considering a vast array of factors, from seasonality and competitive landscape to macroeconomic trends, to make decisions that far exceed human speed and effectiveness. This autonomous, context-aware approach represents a paradigm shift in how advertising campaigns are conceived, executed, and optimized. As a Principal Applied Scientist, you will be at the forefront of this transformation, tackling complex challenges in natural language processing, reinforcement learning, and causal inference. Your pioneering efforts will directly shape the future of e-commerce advertising, with the potential to influence marketplace dynamics on a global scale. This is an unparalleled opportunity to push the boundaries of what's achievable in AI-driven advertising and leave an indelible mark on the industry. Key job responsibilities • Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business using GenAI, LLM, and ML solutions. • Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in AI/ML. • Design and lead organization-wide AI/ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our advertisers. • Work with our engineering partners and draw upon your experience to meet latency and other system constraints. • Identify untapped, high-risk technical and scientific directions, and devise new research directions that you will drive to completion and deliver. • Be responsible for communicating our Generative AI/ Traditional AI/ML innovations to the broader internal & external scientific community.
US, CO, Boulder
Do you want to lead the Ads industry and redefine how we measure the effectiveness of the WW Amazon Ads business? Are you passionate about causal inference, Deep Learning/DNN, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be an Applied Science leader within our Advertising Incrementality Measurement science team! Key job responsibilities As an Applied Science leader within the Advertising Incrementality Measurement (AIM) science team, you are responsible for defining and executing on key workstreams within our overall causal measurement science vision. In particular, you will lead the science development of our Deep Neural Net (DNN) ML model, a foundational ML model to understand the impact of individual ad touchpoints for billions of daily ad touchpoints. You will work on a team of Applied Scientists, Economists, and Data Scientists to work backwards from customer needs and translate product ideas into concrete science deliverables. You will be a thought leader for inventing scalable causal measurement solutions that support highly accurate and actionable causal insights--from defining and executing hundreds of thousands of RCTs, to developing an exciting science R&D agenda. You will solve hard problems, advance science at Amazon, and be a leading innovator in the causal measurement of advertising effectiveness. In this role, you will work with a team of applied scientists, economists, engineers, product managers, and UX designers to define and build the future of advertising causal measurement. You will be working with massive data, a dedicated engineering team, and industry-leading partner scientists. Your team’s work will help shape the future of Amazon Advertising.
US, WA, Seattle
The Seller Fees organization drives the monetization infrastructure powering Amazon's global marketplace, processing billions of transactions for over two million active third-party sellers worldwide. Our team owns the complete technical stack and strategic vision for fee computation systems, leveraging advanced machine learning to optimize seller experiences and maintain fee integrity at unprecedented scale. We're seeking an exceptional Applied Scientist to push the boundaries of large-scale ML systems in a business-critical domain. This role presents unique opportunities to • Architect and deploy state-of-the-art transformer-based models for fee classification and anomaly detection across hundreds of millions of products • Pioneer novel applications of multimodal LLMs to analyze product attributes, images, and seller metadata for intelligent fee determination • Build production-scale generative AI systems for fee integrity and seller communications • Advance the field of ML through novel research in high-stakes, large-scale transaction processing • Develop SOTA causal inference frameworks integrated with deep learning to understand fee impacts and optimize seller outcomes • Collaborate with world-class scientists and engineers to solve complex problems at the intersection of deep learning, economics, and large business systems. If you're passionate about advancing the state-of-the-art in applied ML/AI while tackling challenging problems at global scale, we want you on our team! Key job responsibilities Responsibilities: . Design measurable and scalable science solutions that can be adopted across stores worldwide with different languages, policy and requirements. · Integrate AI (both generative and symbolic) into compound agentic workflows to transform complex business systems into intelligent ones for both internal and external customers. · Develop large scale classification and prediction models using the rich features of text, image and customer interactions and state-of-the-art techniques. · Research and implement novel machine learning, statistical and econometrics approaches. · Write high quality code and implement scalable models within the production systems. · Stay up to date with relevant scientific publications. · Collaborate with business and software teams both within and outside of the fees organization.
US, WA, Seattle
The Selling Partner Experience (SPX) organization strives to make Amazon the best place for Selling Partners to do business. The SPX Science team is building an AI-powered conversational assistant to transform the Selling Partner experience. The Selling Assistant is a trusted partner and a seasoned advisor that’s always available to enable our partners to thrive in Amazon’s stores. It takes away the cognitive load of selling on Amazon by providing a single interface to handle a diverse set of selling needs. The assistant always stays by the seller's side, talks to them in their language, enables them to capitalize on opportunities, and helps them accomplish their business goals with ease. It is powered by the state-of-the-art Generative AI, going beyond a typical chatbot to provide a personalized experience to sellers running real businesses, large and small. Do you want to join an innovative team of scientists, engineers, product and program managers who use the latest Generative AI and Machine Learning technologies to help Amazon create a delightful Selling Partner experience? Do you want to build solutions to real business problems by automatically understanding and addressing sellers’ challenges, needs and opportunities? Are you excited by the prospect of contributing to one of Amazon’s most strategic Generative AI initiatives? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities - Use state-of-the-art Machine Learning and Generative AI techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. - Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. - Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. - Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model validation and model implementation. - Research and implement novel machine learning and statistical approaches. - Participate in strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. About the team Selling Partner Experience Science is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. We are focused on building seller facing AI-powered tools using the latest science advancements to empower sellers to drive the growth of their business. We strive to radically simplify the seller experience, lowering the cognitive burden of selling on Amazon by making it easy to accomplish critical tasks such as launching new products, understanding and complying with Amazon’s policies and taking actions to grow their business.
US, WA, Seattle
Join us in the evolution of Amazon’s Seller business! The Selling Partner Growth organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources. We are looking for a Senior Applied Scientist to lead us to identify data-driven insight and opportunities to improve our SP growth strategy and drive new seller success. As a successful applied scientist on our talented team of scientists and engineers, you will solve complex problems to identify actionable opportunities, and collaborate with engineering, research, and business teams for future innovation. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in ML modeling and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research). Key job responsibilities As a Sr. Applied Scientist in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems via principled statistical solutions (e.g. ML, causal, RL). - Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in MLOps. - Design and lead roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. - Work with our engineering partners and draw upon your experience to meet latency and other system constraints. - Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. - Be responsible for communicating our science innovations to the broader internal & external scientific community.
US, CA, Sunnyvale
Our team leads the development and optimization of on-device ML models for Amazon's hardware products, including audio, vision, and multi-modal AI features. We work at the critical intersection of ML innovation and silicon design, ensuring AI capabilities can run efficiently on resource-constrained devices. Currently, we enable production ML models across multiple device families, including Echo, Ring/Blink, and other consumer devices. Our work directly impacts Amazon's customer experiences in consumer AI device market. The solutions we develop determine which AI features can be offered on-device versus requiring cloud connectivity, ultimately shaping product capabilities and customer experience across Amazon's hardware portfolio. This is a unique opportunity to shape the future of AI in consumer devices at unprecedented scale. You'll be at the forefront of developing industry-first model architectures and compression techniques that will power AI features across millions of Amazon devices worldwide. Your innovations will directly enable new AI features that enhance how customers interact with Amazon products every day. Come join our team! Key job responsibilities As a Principal Applied Scientist, you will: • Own the technical architecture and optimization strategy for ML models deployed across Amazon's device ecosystem, from existing to yet-to-be-shipped products. • Develop novel model architectures optimized for our custom silicon, establishing new methodologies for model compression and quantization. • Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision, language, and audio tasks. • Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs. • Spend the majority of your time doing deep technical work - developing novel ML architectures, writing critical optimization code, and creating proof-of-concept implementations that demonstrate breakthrough efficiency gains. • Influence architecture decisions impacting future silicon generations, establish standards for model optimization, and mentor others in advanced ML techniques.