Careers

Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! At Amazon's Supply Chain Optimization Technologies (SCOT), we are tasked with optimizing the fulfillment on customer orders so that we fulfill all orders worldwide in the most intelligent manner while ensuring Amazon customers get their orders on time. SCOT- Fulfillment Optimization (FO) owns and operates OR/ML and simulation systems that continually optimize the distribution of tens of millions of products across Amazon’s warehouses in the most cost-effective manner, utilizing large scale optimization techniques and distributed computing in trying to reduce overall transportation costs while improving the customer experience. We are focused on saving hundreds of millions of dollars using Optimization, machine learning, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply. We’re looking for a passionate, results-oriented, and inventive scientist who can create and improve models for our outbound transportation planning systems. In addition, you will be working on design, development and evaluation of highly innovative models for solving complex business problems in the area of outbound transportation planning and execution systems. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create optimization solutions to solve those problems at scale. You will directly impact our direct customers, and even play with big data and incredible scale in the background.

Do you want to use your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If you do, People eXperience Technology Central Science (PXTCS) would love to talk to you about how to make that a reality. PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that both improve Amazonian’s wellbeing and their ability to deliver value for Amazon’s customers. We work with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world. As an applied scientist on our team, you will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, define the science vision and translate it into specific plans for applied scientists, as well as engineering and product teams. You will partner with scientists, economists, and engineers on the design, development, testing, and deployment of scalable ML and econometric models. This is a unique, high visibility opportunity for someone who wants to have impact, dive deep into large-scale solutions, enable measurable actions on the employee experience, and work closely with scientists and economists. This role combines science leadership, organizational ability, and technical strength. Key job responsibilities Do you want to leverage your expertise in machine learning and data science to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Central Science (PXTCS) would love to discuss how you can make that a reality. PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world. As an Applied Scientist, you will be responsible for developing and implementing machine learning solutions across our predictive modeling and forecasting work-streams. You will work on existing models and develop new ones that power leaders across Amazon to make decisions about their businesses. You will collaborate with scientists and engineers to deliver innovative solutions while working closely with business stakeholders to understand their needs. You will work across different business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), using various modeling approaches (linear, tree, deep neural network, and LLM-based). You will develop end-to-end ML solutions from problem formulation to deployment, while maintaining high scientific standards and technical excellence.

Are you excited by the idea of developing algorithms to improve the shopping experience for Amazon customers? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art modeling techniques? Join us and you'll help make the shopping experience better for millions of customers while also advancing the state of Amazon's science through publishing research! Key job responsibilities - Develop and apply new machine learning algorithms - Use expertise in supervised learning and causal inference to improve ML performance - Scale optimization techniques to drive business value - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Present and publish science research, contributing to Amazon's science community - Mentor junior engineers and scientists. - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

Project Kuiper is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Project Kuiper will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. Kuiper's Commercial Revenue Operations (CRO) team is looking for a Senior Data Scientist to work hands on from concept to delivery on statistical analysis, prescriptive and predictive analysis, and machine learning implementation projects. We are looking for a problem solver with strong analytical skills and a solid understanding of statistics & Machine learning algorithm as well as a practical understanding of collecting, assembling, cleaning and setting up disparate data from enterprise systems to build models to help solve complex problems in the areas of sales forecasting, customer segmentation, what if simulations and demand forecasting. Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Develop ML models to measure long term impact of seller behaviors • Collaborate with product and engineering teams both within and outside of the CRO team to launch models and develop insights. • Use optimization, statistical, machine learning and analytical techniques to create scalable solutions for business problems. • Design, development and evaluation of highly innovative models for forecast, optimization and experimentation. • Research, experiment and implement novel approaches. • Work closely with other scientists in the team and across teams. • Work and collaborate effectively with product managers and software engineering teams to build algorithms and models and integrate successful models and algorithms in production systems. • Use the best practices in science: data integrity, design, test, and implementation and documentation. • Mentor and guide junior members in the team. • Contribute to Amazon's Intellectual Property through patents and internal and external publications About the team Kuiper Commercial Revenue Operations (CRO) is responsible for the business strategy, operations and systems for Kuiper's commercial business across multiple industry segments in multiple countries.

Are you excited about solving complex business problems at scale through Generative Artificial Intelligence (GenAI)? Are you fascinated about the application of Large Language Models (LLMs) on real-life scenarios? Are you looking to invent solutions using Artificial Intelligence (AI)? If so, we are looking for you to fill a challenging position on Amazon's Trustworthy Shopping Experience (TSE) team. At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy, and giving them the confidence that Amazon stands behind every product and will make it right in the rare chance anything goes wrong. We do this in close partnership with our selling partners and empower them with best-in-class tools and expertise required to offer a high-quality selection of compliant products that customers trust. When we do this consistently, we help selling partners grow their business and power their long-term success. As a Senior Applied Scientist on the team, you will be responsible for delivering the science solutions required to automate complex manual investigation processes, especially by leveraging LLMs. You will handle Amazon scale use-cases with significant impact to the cost of serving Customers. Key job responsibilities - You invent and design new solutions for scientifically-complex problem areas and/or opportunities in existing or new business initiatives. - You design experiments and define the science approach to solve critical business use-cases for automating manual work that involves unstructured text, documents, images, symbols, etc. - Your work focuses on ambiguous problem areas at the product level, where the business problem or opportunity may not yet be crisply defined. - You drive or heavily influence the design of scientifically-complex software solutions or systems, for which you personally write significant parts of the critical scientific novelty. - You provide a system-wide view and design guidance for solutions that can be brand new or evolve from existing ones. - You apply and set the example for best practices in software engineering, and systematically peer review code written by your team members. - You set standards and proactively drive components to use and improve on state-of-the-art techniques. - You autonomously drive thoughtful discussions with customers, engineers, and scientist peers, and build consensus on larger projects and factor complex efforts into independent tasks that can be performed by you and others. About the team Investigation technology Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

External job description As a Sr. Applied Scientist, you will be responsible for assessing and optimizing the performance and reliability of our new and emerging category of devices – Kuiper Customer Terminal. Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to un-served and under-served communities around the world. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: - Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes - Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks - Establishing scalable, efficient, automated processes to handle large scale design and data analysis - Conducting research into use conditions, materials and analysis techniques - Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis - Developing, implementing guidelines to continually optimize design processes - Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design - Using of programming languages like Python and Matlab for analytical/statistical analyses and automation - Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials - Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation - Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking create a huge impact as you help build a state-of-the-art recommendation system? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science through your research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Apply supervised and uplift learning techniques to improve ML performance - Research and implement ML and statistical approaches to add value to the business. - Design A/B tests and conduct statistical analysis on their results - Apply machine learning and statistical algorithms to harness enormous volumes of data as we serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present science research, contributing to Amazon's science community - Mentor junior engineers and scientists. A day in the life As a Senior Data Scientist in the MAPLE team, your day might start with a stand-up meeting, aligning priorities with your colleagues. You'll then dive into analyzing the results of a recent A/B test on a new recommendation algorithm you've developed. Midday, you might collaborate with engineers to optimize the implementation of your model for production. In the afternoon, you could find yourself mentoring a junior team member on statistical techniques or presenting your latest findings to business stakeholders. You'll also dedicate time to staying current with the latest research in machine learning and recommendation systems, possibly contributing to an internal tech talk or external publication. Throughout the day, you'll be using your expertise to solve complex problems, turning data into actionable insights that enhance the customer experience on Amazon's platform. About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning, reinforcement learning through human feedback and complex reasoning; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Key job responsibilities - Work backwards from customer problems to research and develop novel machine learning solutions for music and podcast recommendations. Through A/B testing and online experiments done hand-in-hand with engineering teams, you'll implement and validate your ideas and solutions. - Advocate solutions and communicate results, insights and recommendations to stakeholders and partners. - Produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. Stay current with advancements in the field, adapting latest in literature to build efficient and scalable models A day in the life Lead innovation in ML to shape Amazon Music experiences for millions. Collaborate with talented engineers and scientists to guide research and build scalable models across our audio portfolio - music, podcasts, live streaming, and more. Drive experiments and rapid prototyping, leveraging Amazon's data at scale. Innovate daily alongside world-class teams to delight customers worldwide through personalization. About the team The team is responsible for models that underly all of Amazon Music’s recommendations across content types on mobile, web and Alexa. You will collaborate with a team of product managers, applied scientists and software engineers delivering meaningful recommendations, personalized for each of the millions of customers using Amazon Music globally. As a scientist on the team, you will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models. You will work closely with engineering to realize your scientific vision.

This position gives you an opportunity to build metrics that shape Amazon's catalog initiatives world wide. If that rings a bell and if you possess the confidence to navigate through early stage ambiguities, read on. Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the world’s largest e-Commerce products catalog - it powers the online buying experience for customers worldwide so they can find, discover and buy anything they want. Amazon’s customers rely on the completeness, consistency and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. It requires data driven decisions on what product data changes simplify and improve the Customers’ experience. Our team seeks a Sr. Applied Scientist with demonstrated experience in experimentation techniques and causal inference at scale. Our problems include attributing values to actions in complex world of catalog information driving customer behavior. The ideal candidate combines acumen in data science and causal modeling to grapple with these and other challenges and guide decision-making at the highest levels. This is an opportunity to influence catalog quality improvements across Amazon. Key job responsibilities 1. Build models to attribute customer impact to specific LLM generated product data quality improvements. You will need high judgment for balancing cost efficiency of your models with accuracy of the estimates. 2. Partner with Product Managers and Engineering to build and scale new customer experience metrics 3. Build new business metrics in the A/B experimentation platform 4. Guide quality improvement programs by generating actionable insights About the team We enable teams across Amazon to run A/B experiments on product listings through Catalog Experimentation Program. Additionally, using experimentation and causal inference models, we build customer impact metrics for different experiences in Amazon stores world wide. We help Catalog data quality initiatives understand the customer impact of their work streams and influence their priorities to maximize customer benefits.