Careers

Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders

Amazon Economics is seeking Forecasting, Macroeconomics and Finance (FMF) Economist Interns who are passionate about applying time-series econometric methods to solve real-world business challenges. FMF economists interpret and forecast Amazon business dynamics by combining advanced time-series statistical methods with strong economic analysis and intuition. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to forecast business trends and inform strategic decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. Key job responsibilities As an FMF Economist Intern, you'll specialize in time-series econometric analysis to understand, predict, and optimize Amazon's business dynamics. Your responsibilities include: - Analyze large-scale datasets using advanced time-series econometric techniques to solve complex business challenges - Applying frontier methods in time series econometrics, including forecasting models, dynamic systems analysis, and econometric models that combine macro and micro data - Developing formal models to understand past and present business dynamics, predict future trends, and identify relevant risks and opportunities - Building datasets and performing data analysis at scale using world-class data tools - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including analyzing drivers of growth and profitability, forecasting business metrics, understanding how customer experience interacts with external conditions, and evaluating short, medium, and long-term business dynamics - Build and refine comprehensive datasets for in-depth time-series economic analysis - Present complex analytical findings to business leaders and stakeholders

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. The Agentic Customer Experience (CX) org is responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. The Interactive AI Science team, serves as the cornerstone for AI innovation across Amazon Connect, functioning as the sole science team support high impact product including Amazon Q in Connect, Contact Lens and other key initiatives. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLM Agents evaluation and optimization, etc. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow and AWS Sagemaker. The ideal candidate has the ability to understand, implement, innovate on the state-of-the-art Agentic AI based systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. About the team 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.

Amazon LEO 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. Amazon LEO 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. We are seeking a Scientist to own analytics, scientistic research, validation, and development of our phased array systems across customer terminals and satellite-deployed terminals. In this role, you will transform raw test and telemetry data into actionable insights and automated checks that ensure every deployed system performs to expectations. You will build data and ML pipelines that detect calibration issues, quantify array performance, validate new algorithms, and increase the effectiveness, reproducibility, and automation of our antenna, calibration and deployment workflows. This role sits at the intersection of array processing, data science, and systems engineering, working closely with calibration/validation, phased array systems, RF communications, and test engineering teams. Key job responsibilities - Own metrics and data models that describe end-to-end calibration and system performance for customer terminals and satellite-deployed terminals, from factory test through field deployment. - Design and implement ML and statistical methods (e.g., anomaly detection, drift detection, predictive failure models, classification/regression) to identify mis-calibration, degraded performance, and emerging issues in large fleets of terminals and arrays. - Develop visualization and analytics tools (dashboards, reports, interactive notebooks) to help engineers quickly understand array performance, calibration residuals, and system margins across time, geography, and configurations. - Work with antenna, RF systems, and calibration engineers to define quantitative acceptance criteria for calibration and system validation, and encode those criteria into automated checks and workflows. - Design and run experiments and simulations that compare predicted vs. measured performance, closing the loop between link/array models and deployed hardware. - Build and maintain data pipelines that ingest lab data, chamber results, manufacturing test data, and in-field telemetry into secure, high-quality datasets suitable for analysis and ML training. - Prototype, test, and deploy machine learning and analytics applications in the cloud, partnering with software and systems teams to ensure solutions are scalable, maintainable, and integrated into existing tools and monitoring systems. - Provide clear, data-driven recommendations to improve calibration algorithms, test strategies, and system design; communicate findings to technical and non-technical stakeholders. - Mentor engineers and analysts in data best practices, experiment design, and interpretation of calibration and performance metrics. Export Control Requirement: 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. A day in the life You will work with engineers to process large amounts of data and work through full designs to fully understand the Amazon Leo satellites and customer terminals from lab test benches to fully integrated customer terminals. Your tools and systems will prove out the performance of one of the most advanced communication systems ever built. You’ll collaborate with RF and systems engineers, see your models run using real hardware data, and make daily decisions that directly affect the readiness of Leo’s payload and customer terminal products. The data you analyze and scalable systems you build will enable critical data collection, system analysis, and calibration pipelines that ensure Leo hardware performs flawlessly on Earth and in orbit. About the team Our team owns the full performance lifecycle of Leo’s antenna systems from early concept testing and calibration to final product release. We operate at the intersection of RF hardware, software automation, and large-scale system integration. You’ll work closely with antenna, DSP, and system development engineers, contributing to test frameworks, manufacturing support, and performance validation. We leverage AWS tools and scalable software architectures to accelerate development, automate validation, and deliver reliable test systems used across the entire Leo organization. If you want to work on real hardware, influence product performance, and see your work scale to millions of users worldwide this is the place to do it

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. This specific economist role is part of the Automated Profitability Management (APM) team whose vision is to offer the world’s most advanced suite of products to optimize and manage the contractual agreements between a retailer and its suppliers. Our teams build software and machine learning models to optimize contract negotiations, and exchange of goods between Amazon Retail and tens of thousands of brands ("vendors") offering millions of products available to hundreds of millions of Amazon customers worldwide. We handle product costs (How much should Amazon pay for this product?) but also cover supply chain (Who pays the initial shipping costs?), reverse-logistics (Who pays for damaged products? What about unsold inventory?) and more. Our mission is to provide to our customers the world’s largest product assortment at competitive prices, through long term partnership with our vendors. You will build econometrics and machine learning (ML) models that combine internal cost and pricing data, with external inflation signals (e.g. commodity prices, labor rates, transportation costs) to determine where contract changes are warranted. Where possible, you will design and conduct experiments to estimate the long term impact of the new contracts on customers, vendors, and Amazon. To do so, you will work closely with other economists, ML scientists, product managers, and software developers to implement and scale your solutions. Key job responsibilities - Develop econometric and causal models to improve the outcome of contract negotiations for all products offered by Amazon Retail worldwide. - Determine the causal impacts of systems, strategy, or inputs changes on business growth and profitability through experiments or observational methods. - Collaborate with product managers, applied scientists, economists, and software developers to incorporate economic models into negotiation tooling.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-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 enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Selling Partner Trust & Store Integrity Science Team. We are looking for a talented scientist who is passionate to build advanced machine learning systems that help manage the safety of millions of transactions every day and scale up our operation with automation. Key job responsibilities - Innovate with the latest GenAI/LLM/VLM technology to build highly automated solutions for efficient risk evaluation and automated operations - Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to create impactful business value - Learn, explore and experiment with the latest machine learning advancements to create the best customer experience A day in the life you will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence. You'll be working closely with business partners and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Are you ready to be at the forefront of Agentic AI innovation and redefine the future of communication? Join our dynamic Alexa Connections team as a Sr. Applied Scientist, and lead futuristic initiatives that will shape the next generation of intelligent, conversational experiences. In this role, you'll work at the intersection of disruptive AI technologies and real-world impact, making a difference for millions of customers. You'll collaborate with a team of passionate professionals who are as excited about innovation as you are, and together, you'll push the boundaries of what's possible with Alexa+. As a Sr. Applied Scientist, you'll drive the development of novel algorithms and modeling techniques to advance the state of the art with LLMs and real-time Agentic AI solutions that power our next-generation communication features. You'll work closely with cross-functional teams, including product management, engineering, design, and data, to design and deliver innovative solutions that leverage these AI technologies to enable seamless, intelligent communication experiences. You'll also lead the integration of these advanced AI systems into Alexa's core capabilities, ensuring a seamless and intuitive user experience. Key job responsibilities - Develop new inference and training techniques to improve the performance of Large Language Models for Smart Home control and Automation - Develop robust techniques for synthetic data generation for training large models and maintaining model generalization - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environment, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues - Mentoring junior scientists to improve their skills, knowledge, and their ability to get things done About the team Alexa Connections aspires to make Alexa+ the world’s most trusted connection assistant for getting things done and creating moments of joy. Our vision emphasizes a) Trust as our foundation for becoming a daily habit, knowing our customers have plentiful choices, b) Completion of end-to-end customer journeys, beyond shipping features, and c) Joy through personalized, proactive experiences, that create a memory.

The WW DSP Analytics team is a centralized analytics organization within Amazon's Last Mile Delivery Service Partner (DSP) program. We build best-in-class solutions that enable data-driven decision making across our global DSP ecosystem. Our team partners with internal stakeholders, DSP owners, and cross-functional teams to deliver insights that drive operational excellence, business growth, and the success of small business owners in Last Mile delivery. Our work directly impacts customer experience, driver and station associate experience, DSP success, and Amazon's sustainable growth. We are seeking a passionate Data Scientist with strong machine learning and analytical skills to join our team. You will work on challenging problems in the delivery planning space, applying data science rigor to generate actionable insights that support DSP performance measurement and continuous improvement. Key job responsibilities Develop Science Solutions for DSP Performance: Design and implement data science solutions to optimize Delivery Service Partner (DSP) operations, capacity planning, and performance measurement across the global DSP network Apply Advanced Machine Learning Techniques: Leverage solid research experience in Machine Learning and statistical modeling to identify opportunities for improving DSP analytics, forecasting models, and performance measurement systems Optimize DSP Program Policies and Sentiment Risks: Analyze sentiment risks and enhance existing algorithms that support DSP program management, including scorecard metrics, capacity reliability models, and performance evaluation frameworks Analyze Business Requirements with Return on Investment (ROI) calculation: Demonstrate superior logical thinking by quickly approaching large, ambiguous problems, translating high-level DSP program requirements into mathematical models, and applying models to predict the return on investment. Build Production-Scale Analytics: Contribute to the development and deployment of scalable data models, dashboards, and automated reporting systems that enable self-service analytics for DSP stakeholders Accelerate GenAI footprint: Partner with Data Engineers to expand our GenAI tools and improve developer productivity along with raising the bar on data quality. Conduct Independent Data Analysis: Mine and analyze complex datasets across multiple domains (performance metrics, financial data, operational data) using programming and statistical analysis tools to generate actionable insights Thrive in a Collaborative Environment: Excel in a fast-paced analytics organization that encourages collaborative and creative problem-solving, measure and communicate analytical risks, constructively critique peer work, and align research focuses with DSP program strategic needs Partner Cross-Functionally: Work closely with Business Intelligence Engineers, program teams, and DSP stakeholders to define KPIs, validate analytical approaches, and ensure insights drive meaningful business outcomes

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Lead design and implement control algorithms for robot locomotion - Develop behaviors that enable the robot to traverse diverse terrain - Develop methods that seamlessly integrate stability, locomotion, and manipulation tasks - Create dynamics models and simulations that enable sim2real transfer of algorithms - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists