The 10 top articles of 2022

We explored everything from the science behind the new F1 car to a look back at how the operations science team addressed a challenge of almost unimaginable complexity.

The articles below, presented in chronological order, are ones that appealed most to our science- and technology-curious readers.

  1. Learn how a combination of deep learning, natural language processing, and computer vision enables Amazon to hone in on the right amount of packaging for each product. Over the past six years this effort has reduced per-shipment packaging weight by 36% and eliminated more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.

  2. Image shows the 2022 F1 car sitting in profile on a racetrack with viewing stands in the background
    The F1 engineering team collaborated with AWS to explore the science of how cars interact when racing in close proximity.
    F1

    “Nobody designs a car to come in second,” observed Pat Symonds, chief technical officer at FORMULA 1. “But for this project, we were looking at how cars perform in the wake of another car, as opposed to running in clean air.”

    Instead of relying on time-consuming and costly physical tests, F1 used computational fluid dynamics, which provides a virtual environment to study the flow of fluids (in this case the air around the F1 car) without ever having to manufacture a single part.

    Learn how the F1 engineering team collaborated with Amazon Web Services to develop new design specifications to help make races more competitive.

  3. Since 2018, Amazon Music customers in the US who aren’t sure what to choose have been able to converse with Alexa. The technical complexity of this challenge is hard to overstate, but progress in machine learning (ML) at Amazon has recently made the Alexa music recommender experience even more successful and satisfying for customers.

    To achieve that, the Amazon Music Conversations team developed the next-generation of conversation-based music recommender, one that harnesses ML to bring the Alexa music recommender closer to being a genuine, responsive conversation.

    Learn how the Amazon Music Conversations team is using pioneering machine learning to make Alexa's discernment better than ever.

  4. Earlier this year, Amazon Web Services expanded the widely popular MLU course offerings with MLU Explain, a public website containing visual essays that incorporate fun animations and “scrolly-telling” to explain machine learning concepts in an accessible manner.

    “MLU Explain is a series of interactive articles covering core machine learning concepts, and they're meant to provide supplementary material that's educational within a light, but still informative format,” said Jared Wilber, a data scientist who both teaches some of the MLU courses as well as develops fascinating visual explainers (like the one below) for those courses.

    This animation is from an MLU Explains article that explains the Receiver Operating Characteristic Curve (ROC) curve, how it works with a live interactive example, and how it relates to Area Under The Curve (AUC).
    This animation is from an MLU Explains article that explains the Receiver Operating Characteristic Curve (ROC) curve, how it works with a live interactive example, and how it relates to Area Under The Curve (AUC).

    “There are so many people who have very strong technical skills, but who don’t know a ton about machine learning,” he says. “So, our goals for MLU are twofold: the first is to teach machine learning to people who have no experience with how it works and how they can use it, and the second is to help people who already have some experience and want to sharpen their skills.”

    Learn how the MLU Explain articles are helping Wilber and his team meet those goals.

  5. The AI stack at the center of the Zoox driving system broadly consists of three processes, which occur in order: perception, prediction, and planning. These equate to seeing the world and how everything around the vehicle is currently moving, predicting how everything will move next, and deciding how to move from A to B given those predictions.

    “Predicting the future — the intentions and movements of other agents in the scene — is a core component of safe, autonomous driving,” says Kai Wang, director of the Zoox Prediction team.

    This two framed animation shows Zoox's software making predictions about movements on the left, on the right is the camera view of those same pedestrians crossing the street as the vehicle is stopped
    An example of a Zoox vehicle negotiating a busy intersection in Las Vegas at night. The green boxes show the most likely prediction for each agent in the scene as far as 8 seconds into the future.

    Learn how the combination of cutting-edge hardware, sensor technology, and bespoke machine learning approaches can predict trajectories of vehicles, people, and even animals, as far as eight seconds into the future.

  6. “This project uses a combination of various techniques,” said Andrea Qualizza, a SCOT senior principal scientist. “There is mathematical optimization, local search, capacitated vehicle routing problem solvers — all of that came together because these techniques considered various aspects of the problem and linked them very naturally with the way our systems work."

    That project — Customer Order and Network Density OptimizeR or CONDOR — is notable because of it ability to determine the right tradeoff between the levels of complexity and optimality.

    “We can enable carriers to deliver more packages to more customers on time, while reducing miles driven and carbon emissions from fuel,” Qualizza said. “That is the essence of CONDOR; it revisits all those decisions and finds those opportunities for us to further delight customers.”

    Learn how CONDOR has led to improved delivery routes.

  7. For humans, finding and fetching a bottle of ketchup from a cluttered refrigerator without toppling the milk carton is a routine task. For robots, this remains a challenge of epic complexity.

    At Amazon, scientists are addressing this challenge by teaching robots to understand cluttered environments in three dimensions, locate specific items, and safely retrieve them using a move called the pinch grasp — that unique thumb-and-finger hold that many people take for granted.

    Watch the pinch grasping arm sort through items

    “In robotics, we don’t have the mechanical ability of a five-finger dexterous hand,” said Aaron Parness, a senior manager for applied science at Amazon Robotics AI. “But we are starting to get some of the ability to reason and think about how to grasp. We’re starting to catch up. Where pinch-grasping is really interesting is taking something mechanically simple and making it highly functional.”

    Learn how the pinch grasping robot achieved a ten-fold reduction in damage on items such as books and boxes in tests.

  8. Amazon’s Supply Chain Optimization Technologies organization 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 quantitiesallocating 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.

    “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.

    Learn how the SCOT team has evolved over time to meet a challenge of staggering complexity.

  9. 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.

    The stowing process

    “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.” Robots and humans working side by side is key to the long-term expansion of this technology beyond retail, said Aaron Parness, Robotics AI senior manager of applied science.

    “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.”

    Learn how Amazon Robotics researchers achieved a result that, until recently, was impossible.

  10. When an item comes into an Amazon fulfillment center, employees use barcodes to verify its identity at several different points along its journey to a delivery vehicle. Each time, the item has to be picked up and the barcode located and scanned. Sometimes, the barcode is damaged or even missing.

    Using modalities to generate a digital fingerprint

    That process is repeated millions of times across a massive catalogue of items of varying shapes and sizes, and it can’t easily be automated. Right now, there isn’t a robot versatile enough to manipulate any item that may come into a warehouse and then scan it.

    The solution? Augment or even eliminate the barcode. Or, better still, eliminate the reliance on awkward and inefficient manual item identification altogether.

    That’s what Amazon is researching using multimodal identification, or MMID. This process uses multiple modalities of information — for example, extracting the appearance and dimensions of an item from an image of that item — to automate identification.

    Learn how Amazon researchers are working to eliminate the need for barcodes.

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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.
US, MA, Westborough
Amazon is looking for talented Postdoctoral Scientists to join our Fulfillment Technology and Robotics team for a one-year, full-time research position. The Innovation Lab in BOS27 is a physical space in which new ideas can be explored, hands-on. The Lab provides easier access to tools and equipment our inventors need while also incubating critical technologies necessary for future robotic products. The Lab is intended to not only develop new technologies that can be used in future Fulfillment, Technology, and Robotics products but additionally promote deeper technical collaboration with universities from around the world. The Lab’s research efforts are focused on highly autonomous systems inclusive of robotic manipulation of packages and ASINs, multi-robot systems utilizing vertical space, Amazon integrated gantries, advancements in perception, and collaborative robotics. These five areas of research represent an impactful set of technical capabilities that when realized at a world class level will unlock our desire for a highly automated and adaptable fulfillment supply chain. As a Postdoctoral Scientist you will be developing a coordinated multi-agent system to achieve optimized trajectories under realistic constraints. The project will explore the utility of state-of-the-art methods to solve multi-agent, multi-objective optimization problems with stochastic time and location constraints. The project is motivated by a new technology being developed in the Innovation Lab to introduce efficiencies in the last-mile delivery systems. Key job responsibilities In this role you will: * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise.
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, 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.
US, CA, Palo Alto
Amazon’s Advertising Technology team builds the technology infrastructure and ad serving systems to manage billions of advertising queries every day. The result is better quality advertising for publishers and more relevant ads for customers. In this organization you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading companies. Amazon Publisher Services (APS) helps publishers of all sizes and on all channels better monetize their content through effective advertising. APS unites publishers with advertisers across devices and media channels. We work with Amazon teams across the globe to solve complex problems for our customers. The end results are Amazon products that let publishers focus on what they do best - publishing. The APS Publisher Products Engineering team is responsible for building cloud-based advertising technology services that help Web, Mobile, Streaming TV broadcasters and Audio publishers grow their business. The engineering team focuses on unlocking our ad tech on the most impactful Desktop, mobile and Connected TV devices in the home, bringing real-time capabilities to this medium for the first time. As a successful Data Scientist in our team, · You are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You will collaborate directly with product managers, BIEs and our data infra team. · You will analyze large amounts of business data, automate and scale the analysis, and develop metrics (e.g., user recognition, ROAS, Share of Wallet) that will enable us to continually measure the impact of our initiatives and refine the product strategy. · Your analytical abilities, business understanding, and technical aptitude will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. · You will have direct exposure to senior leadership as we communicate results and provide scientific guidance to the business. Major responsibilities include: · Utilizing code (Apache, Spark, Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems. · Collaborate with product, BIEs, software developers, and business leaders to define product requirements and provide analytical support · Build customer-facing reporting to provide insights and metrics which track system performance · Influence the product strategy directly through your analytical insights · Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
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, Seattle
Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success.. Come join the team that owns the technology behind AWS People Planning products, services, and metrics. We leverage technology to improve the experience of AWS Executives, HR/Recruiting/Finance leaders, and internal AWS planning partners. A Sr. Data Scientist in the AWS Workforce Planning team, will partner with Software Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities * Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams * Priority management - manage diverse requests and dependencies from teams * Process improvements – define, implement and continuously improve delivery and operational efficiency * Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations * Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better.
PL, Warsaw
Come build the future of smart security with us. Are you interested in helping shape the future of devices and services designed to keep people close to what’s important? The Senior Data Scientist within Ring Data Science and Engineering plays a pivotal role in better understanding how customers interact with our products and how we can improve their experience. This role will build scalable solutions and models to support our business functions (Subscriptions, Product, Customer Service). By leveraging a range of methods with an emphasis on causal techniques, you will explain, quantify, predict and prescribe in support of informing critical business decisions. You will help the organization better understand customers and how to best impact them. You will seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders. Key job responsibilities - Lead development and validation of state-of-the-art technical designs (causal inference, predictive tabular models, data insights/visualizations from EDA, etc) - Drive shared understanding among business, engineering, and science teams of domain knowledge of processes, system structures, and business requirements. - Apply domain knowledge to identify product roadmap, growth, engagement, and retention opportunities; quantify impact; and inform prioritization. - Advocate technical solutions to business stakeholders, engineering teams, and executive level decision makers. - Contribute to the hiring and development of others - Communicate strategy, progress, and impact to senior leadership A day in the life Translate/Interpret - Complex and interrelated datasets describing customer behavior, messaging, content, product design and financial impact. Measure/Quantify/Expand - Apply statistical or machine learning knowledge to specific business problems and data. - Analyze historical data to identify trends and support decision making. - Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. - Provide requirements to develop analytic capabilities, platforms, and pipelines. Explore/Enlighten - Make decisions and recommendations. - Build decision-making models and propose solution for the business problem you defined. Help productionalize them so they can be used systemically. - Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication. - Utilize code (Python/R/SQL) for data analyzing and modeling algorithms. About the team We started in a garage in 2012 when our founder asked a simple question: what if you could answer the front door from your phone? What if you could be there without needing to actually, you know, be there? After many late nights and endless tinkering, our first Video Doorbell was born. That invention has grown into over a decade of groundbreaking products and next-level features. And at the core of all that, everything we’ve done and everything we’ve yet to build, is that same inventor's spirit and drive to bridge the distance between people and what they care about. Whatever it is, at Ring we’re committed to helping you be there for it. (https://www.ring.com)
US, WA, Bellevue
Why this job is awesome? This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. - Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? - Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems? - Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? - Do you like to innovate and simplify? If yes, then you may be a great fit to join the DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation