Stories that inspired us in 2022

From the remarkable story of Josh Miele and his passion for improving accessibility, to the challenges overcome by scientists and engineers getting Alexa to work in space, these five stories touched both emotional and intellectual chords.

  1. In September of last year, Josh Miele, an Amazon principal accessibility researcher, got a text from someone at the MacArthur Foundation requesting a phone call, his heart leapt. For anyone in the arts and sciences, a MacArthur Fellowship, known as the “genius” grant, is akin to winning the lottery. You can’t apply for the $625,000 fellowship; it just arrives, mysteriously, out of the ether with a phone call from the foundation.

    Josh Miele, in a purple dress shirt, sits at a desk in an office, he is typing and looking at a computer screen, there are chairs and desks in the background
    Josh Miele, an Amazon principal accessibility researcher, was selected a 2021 MacArthur Foundation Fellow. He has spent his career developing tools to make the world more accessible for people who are blind and visually impaired.
    Meg Coyle / Amazon

    For Miele, who is blind and has spent his career developing tools to make the world more accessible for people who are blind and visually impaired, a MacArthur grant had long been a fantastical dream.

    Yet given his life’s work, the grant was a surprise to no one who knows Miele. After graduating from the University of California, Berkeley with a PhD in psychoacoustics in 2003, Miele worked for 16 years at the non-profit Smith-Kettlewell Eye Research Institute in San Francisco as a principal scientist and researcher.

    Learn what he's doing at Amazon to foster accessibility for the blind and visually impaired.

  2. About 16 years ago, Sergey Menis, now a senior startup solution architect with AWS, was volunteering at a protein design lab during the day and parking cars at night. He'd come to the Baker Lab in Seattle on a whim. While earning his master's degree in bioinformatics at Chalmers University of Technology in Sweden, he read a 2003 paper describing the lab's work designing a novel protein that didn't exist in nature.

    Sergey Menis is seen in a lab setting, wearing gloves while holding a device
    Sergey Menis said when he read about David Baker's work in inventing a protein molecule from scratch, the next chapter of his career — computational protein design — began to unfold.
    Courtesy of Sergey Menis

    "I was just in awe of that power," Menis recalled. He wanted to learn more about biochemist David Baker's work and emailed asking to join the lab, which is based at the University of Washington. Once there, he opted to work with Bill Schief, a postdoctoral researcher with Baker who was just starting his own lab.

    Schief and his team, along with Menis, developed a breakthrough approach to a vaccine for HIV. In February 2021, the nonprofit scientific research organizations IAVI and Scripps Research announced exciting results in a phase 1 clinical trial — called IAVI G001 — of the Schief lab's vaccine candidate.

    Earlier this year, building on those results, IAVI and Moderna announced that first doses had been administered in a new clinical trial of the experimental HIV vaccine.

    Learn about the role Menis played in helping bring the world closer to a human immunodeficiency virus vaccine.

  3. SuaCode, cofounded by piloted by former Alexa AI intern George Boateng, teaches young students in Africa to code using Android devices and a bilingual (English and French) AI-powered teaching assistant, Kwame, named after Ghana’s first president, Kwame Nkrumah.

    George Boateng presenting his PhD research at the second Black in AI workshop at NeurIPS 2018 in Montréal, Canada
    George Boateng presenting his PhD research at the second Black in AI workshop at NeurIPS 2018 in Montréal, Canada.
    George Boateng

    To date, SuaCode has introduced more than 2,000 learners from 42 African countries to the fundamentals of software. Boateng and his team are currently developing additional courses and partnering with universities across Africa to host and deliver programming through the SuaCode platform.

    While at Amazon, Boateng and his colleagues developed a taxonomy of incongruity and expression in sarcastic utterances and performed systematic error analysis towards the goal of sarcasm detection. A paper is currently in the works.

    “We didn’t completely solve sarcasm detection,” Boateng wrote on LinkedIn. “But we have taken a giant step towards that goal.”

    Learn what else Boateng worked on in his Amazon internship and what inspires him to see solutions in scenarios where many people see problems.

  4. In a world of ideal sustainability, every customer order received by Amazon that required a box would ship in a box tailored precisely to the size of its contents to minimize cardboard waste. But with an ever-changing catalogue of hundreds of millions of items and multiple items often shipped in a shared box, this dream scenario would require a near-infinite range of box sizes standing ready at Amazon’s fulfillment centers (FCs).

    An animation shows a stack of boxes slowly reducing in number to arrive at an optimal suite of boxes for packing items as part of Amazon's PackOpt system
    By the end of 2022, about 90% of all boxes shipped by Amazon will be sent from an optimized box suite, thanks to implementation of the pioneering web-based PackOpt tool.

    “This problem belongs to a theoretical class of problems called ‘NP hard’: essentially, no one knows if there's a really efficient algorithm to solve them,” said Renan Garcia, a principal research scientist. The solution? A pioneering web-based tool called PackOpt.

    By the end of this year, about 90% of all boxes shipped by Amazon will be sent from an optimized box suite. In North America, PackOpt technology has resulted in an annual reduction in cardboard waste of 7% to 10%, saving roughly 60,000 tons of cardboard annually. In emerging countries such as Singapore, PackOpt has delivered more than double that percentage efficiency.

    Learn about the science behind PackOpt and how it's helping Amazon deliver on its Climate Pledge commitment.

  5. On Dec. 11, the Orion spacecraft splashed down in the Pacific Ocean, after traveling more than 1.4 million miles around the Moon and returning safely to Earth. NASA administrator Bill Nelson noted the splashdown "occurred 50 years to the day of the Apollo 17 Moon landing." Aboard Orion — the first human-rated spacecraft to visit the moon in more than 40 years — was an Echo device.

    NASA’s Space Launch System rocket carrying the Orion spacecraft launches on the Artemis I flight test, Wednesday, Nov. 16, 2022, from Launch Complex 39B at NASA’s Kennedy Space Center in Florida.
    NASA’s Space Launch System rocket carrying the Orion spacecraft launches on the Artemis I flight test, Wednesday, Nov. 16, 2022, from Launch Complex 39B at NASA’s Kennedy Space Center in Florida.
    NASA/Joel Kowsky

    Setting up an Echo device on Earth is simple: all you need is a Wi-Fi connection and the Alexa app. However, things are far more complicated in space. For example, one challenge stemmed from the lack of humans on board. For Orion, commands to Alexa have to be sent from ground control. The low-bandwidth connections utilized for the transmission can make it challenging to transmit voices at the wide range of frequencies essential for differentiating between sounds.

    Learn how scientists and engineers overcame that challenge (and several others) to get Alexa to work in space.

Related content

GB, London
Economic Decision Science is a central science team working across a variety of topics in the EU Stores business and beyond. We work closely EU business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with EU- and US-based interdisciplinary teams. We are looking for a Senior Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations and analytics teams to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities - Provide data-driven guidance and recommendations on strategic questions facing the EU Retail leadership - Scope, design and implement version-zero (V0) models and experiments to kickstart new initiatives, thinking, and drive system-level changes across Amazon - Build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challenges - Influence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change We are open to hiring candidates to work out of one of the following locations: London, GBR
US, WA, Seattle
The JP Economics and Decision Science Team is looking for an Intern Economist with experience in empirical economic analysis to conduct research on the impact evaluation and prediction of marketing campaigns in Amazon Japan's online retail business. The successful candidate will work closely with the team to improve the efficiency of designing marketing campaigns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics and applied microeconomics and familiarity with Stata, R, or Python are necessary. Experience with SQL would be a plus, but not required. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will work in a team of economists, data scientists, and engineers and in collaboration with product and finance managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities • Use regression analysis to estimate econometric models and develop forecasting solutions that can predict marketing campaign effectiveness. • Collaborate with other economists and data scientists to validate and refine the econometric models. • Work with product managers and software developers to integrate the forecasting models into the campaign management system. • Monitor the accuracy and effectiveness of the forecasting models and make adjustments as necessary. • Communicate your findings and recommendations to team members and stakeholders. A day in the life - Discussions with business partners, as well as product managers and tech leaders to understand the business problem. - Brainstorming with other scientists and economists to design the right model for the problem in hand. - Present the results and new ideas for existing or forward looking problems to leadership. - Deep dive into the data. - Modeling and creating working prototypes. - Analyze the results and review with partners. About the team We are a team of economists, data scientists, and business intelligence engineers supporting Amazon Japan's Customer Growth and Engagement (CGE) org as the one-stop data science enabler. We use analytical insights and products to empower CGE and align strategic decisions across partner teams (e.g., Operations, Delivery Experience, Pricing). We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Senior Applied Scientist, with expertise in machine learning and a proven record of solving business problems through scalable ML solutions, to join our top-notch cross-domain FBA science team. We want to learn seller behaviors, understand seller experience, build automated LLM-based solutions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. We also predict potentially costly defects that may occur during packing, shipping, receiving and storing the inventory. We aim to prevent such defects before occurring while we are also fulfilling customer demand as quickly and efficiently as possible, in addition to managing returns and reimbursements. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. As a senior applied scientist, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, and reinforcement learning. This role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate scientific work into production systems. Key job responsibilities - As a senior member of the science team, you will play an integral part in building Amazon's FBA management system. - Research and develop machine learning models to solve diverse business problems faced in Seller inventory management systems. - Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for research and applied scientists, as well as engineering and product teams. - Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. - Review and audit modeling processes and results for other scientists, both junior and senior. - Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. Your solutions may lead to billions of dollars impact on either the topline or the bottom line of Amazon third-party seller business. As a senior scientist on the team, you will be involved in every aspect of the process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. You are expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by mathematical proof. You will also collaborate with the broader decision and research science community in Amazon to broaden the horizon of your work and mentor engineers and scientists. The successful candidate will have the strong expertise in applying machine learning models in an applied environment and is looking for her/his next opportunity to innovate, build, deliver, and impress. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. The candidate will need to be entrepreneurial, wear many hats, and work in a fast-paced, high-energy, highly collaborative environment. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Bellevue
The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Applied Scientist, with expertise and experience in machine learning, to join our top-notch cross-domain FBA science team. We want to learn seller behaviors, understand seller experience, build automated LLM-based solutions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. We also predict potentially costly defects that may occur during packing, shipping, receiving and storing the inventory. We aim to prevent such defects before occurring while we are also fulfilling customer demand as quickly and efficiently as possible, in addition to managing returns and reimbursements. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. As an applied scientist, you will design and implement ML solutions that will likely draw from a range of scientific areas such as supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, and reinforcement learning. This role has high visibility to senior Amazon business leaders and involves working with other senior and principal scientists, and partnering with engineering and product teams to integrate scientific work into production systems. Key job responsibilities - Research and develop machine learning models to solve diverse FBA business problems. - Translate business requirements/problems into specific plans for research and applied scientists, as well as engineering and product teams. - Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. - Work closely with teams of scientists, product managers, program managers, software engineers to drive production model implementations. - Build scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers A day in the life In this role, you will work in machine learning with significant scope, impact, and high visibility. Your solutions may lead to billions of dollars impact on either the topline or the bottom line of Amazon third-party seller business. As an applied scientist, you will be involved in every aspect of the scientific development process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. You are expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by mathematical proof. You will also collaborate with the broader decision and research science community in Amazon to broaden the horizon of your work and mentor engineers and scientists. The successful candidate will have the strong expertise in applying machine learning models in an applied environment and is looking for her/his next opportunity to innovate, build, deliver, and impress. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Seattle
Outbound Communications own the worldwide charter for delighting our customers with timely, relevant notifications (email, mobile, SMS and other channels) to drive awareness and discovery of Amazon’s products and services. We meet customers at their channel of preference with the most relevant content at the right time and frequency. We directly create and operate marketing campaigns, and we have also enabled select partner teams to build programs by reusing and extending our infrastructure. We optimize for customers to receive the most relevant and engaging content across all of Amazon worldwide, and apply the appropriate guardrails to ensure a consistent and high-quality CX. Outbound Communications seek a talented Applied Scientist to join our team to develop the next generation of automated and personalized marketing programs to help Amazon customers in their shopping journeys worldwide. Come join us in our mission today! Key job responsibilities As an Applied Scientist on the team, you will lead the roadmap and strategy for applying science to solve customer problems in the automated marketing domain. This is an opportunity to come in on Day 0 and lead the science strategy of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive messaging experiences. You will be part of a multidisciplinary team and play an active role in translating business and functional requirements into concrete deliverables. You will work closely with product management and the software development team to put solutions into production. You will apply your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. You will have a unique opportunity to produce and deliver models that help build best-in-class customer experiences and build systems that allow us to deploy these models to production with low latency and high throughput. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (Gen AI) in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and Gen AI in Computer Vision, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN
US, WA, Seattle
The Automated Reasoning Group in AWS Platform is looking for an Applied Scientist with experience in building scalable solver solutions that delight customers. You will be part of a world-class team building the next generation of automated reasoning tools and services. AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. You will apply your knowledge to propose solutions, create software prototypes, and move prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You will use your strong verbal and written communication skills, are self-driven and own the delivery of high quality results in a fast-paced environment. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. See https://aws.amazon.com/security/provable-security/ As an Applied Scientist in AWS Platform, you will play a pivotal role in shaping the definition, vision, design, roadmap and development of product features from beginning to end. You will: - Define and implement new solver applications that are scalable and efficient approaches to difficult problems - Apply software engineering best practices to ensure a high standard of quality for all team deliverables - Work in an agile, startup-like development environment, where you are always working on the most important stuff - Deliver high-quality scientific artifacts - Work with the team to define new interfaces that lower the barrier of adoption for automated reasoning solvers - Work with the team to help drive business decisions The AWS Platform is the glue that holds the AWS ecosystem together. From identity features such as access management and sign on, cryptography, console, builder & developer tools, to projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second. Learn and Be Curious. We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members. Inclusion and Diversity. Our team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more. Key job responsibilities Work closely with internal and external users on defining and extending application domains. Tune solver performance for application-specific demands. Identify new opportunities for solver deployment. About the team Solver science is a talented team of scientists from around the world. Expertise areas include solver theory, performance, implementation, and applications. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Portland, OR, USA | Seattle, WA, USA
US, WA, Seattle
We’re working to improve shopping on Amazon using the conversational capabilities of LLMs, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, across the breadth of Amazon Shopping and AGI to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
We are looking for an Applied Scientist to join our Seattle team. As an Applied Scientist, you are able to use a range of science methodologies to solve challenging business problems when the solution is unclear. Our team solves a broad range of problems ranging from natural knowledge understanding of third-party shoppable content, product and content recommendation to social media influencers and their audiences, determining optimal compensation for creators, and mitigating fraud. We generate deep semantic understanding of the photos, and videos in shoppable content created by our creators for efficient processing and appropriate placements for the best customer experience. For example, you may lead the development of reinforcement learning models such as MAB to rank content/product to be shown to influencers. To achieve this, a deep understanding of the quality and relevance of content must be established through ML models that provide those contexts for ranking. In order to be successful in our team, you need a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as SageMaker, S3, and EC2 with a variety of skillset in shallow and deep learning ML models, particularly in NLP and CV. You will bring knowledge in many of these domains along with your own specialties. Key job responsibilities • Use statistical and machine learning techniques to create scalable and lasting systems. • Analyze and understand large amounts of Amazon’s historical business data for Recommender/Matching algorithms • Design, develop and evaluate highly innovative models for NLP. • Work closely with teams of scientists and software engineers to drive real-time model implementations and new feature creations. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation. • Research and implement novel machine learning and statistical approaches, including NLP and Computer Vision A day in the life In this role, you’ll be utilizing your NLP or CV skills, and creative and critical problem-solving skills to drive new projects from ideation to implementation. Your science expertise will be leveraged to research and deliver often novel solutions to existing problems, explore emerging problems spaces, and create or organize knowledge around them. About the team Our team puts a high value on your work and personal life happiness. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of you. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to establish your own harmony between your work and personal life. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Seattle, WA, USA