Careers

Amazon is looking for talented Postdoctoral Scientists to join our Robotics team for a one-year, full-time research position. Postdoctoral Scientists will innovate in key areas of computer vision and manipulation challenges in Amazon warehouses. The Amazon Robotics team seeks to automate the picking activities in Amazon AR Sortable FCs by retrofitting into existing stations. The team develops robotic manipulators, both the related hardware and software (including machine learning approaches). The team was formed in 2022 and is based in Berlin. Our team focuses on solving computer vision and manipulation challenges in Amazon warehouses. For example, how can robots interact with the fabric pods used to store items. The technical challenges include 3D scene understand for items in clutter, identification of target items in the scene, and placement and/or removal of target items. As the solution space is open ended, the team is looking for postdoc candidates who are excited to apply and advance the state-of-the-art algorithms to this domain. 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 vibrant and 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 cutting-edge techniques in your area(s) of expertise.

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As a Senior Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including machine learning, artificial intelligence (AI), natural language processing (NLP), reinforcement learning (RL), real-time and distributed systems. Your primary focus will be on designing, developing, and deploying highly innovative modeling techniques to production to advance the state of the art in aforementioned domains. Your decision-making will consistently incorporate robust, data-driven business and technical judgment. ABOUT YOU You have a background in modern programming languages, distributed system design, service-oriented architecture, and high scalability. Experience in advanced machine learning technologies and Artificial intelligence is a big plus. Equally important is the ability to multi-task, invent, create reliable and maintainable code, and find creative, scalable solutions to difficult problems. You'll work with experienced managers who'll care for you. We'll guide you on your career growth path and there's no shortage of technical challenges. As a Senior Applied Scientist, you will... - Understand large complex use cases across the business and design scalable, efficient, automated model solutions - Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features - Drive and lead strategic initiatives to employ the most recent advances in ML/AI in a fast-paced, experimental environment - Mentor and grow the scientists in the team and across Amazon ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

Are you ready to make a global impact with your expertise in Machine Learning and Statistics? Amazon is seeking an innovative and driven Applied Scientist to join our team and help shape the future of cutting-edge technology. In this role, you will design, develop, and deploy state-of-the-art machine learning models to address some of the most complex and meaningful challenges in the digital world. Your work will directly enhance the experiences of millions of customers on the world’s largest online retail platform. As a member of the Operations Risk and Compliance (ORC) Science team, your mission will be to revolutionize product classification for every item sold on Amazon’s platform, ensuring seamless compliance with public authority regulations worldwide. Our work is at the intersection of cutting-edge machine learning and real-world impact, driving Amazon's commitment to operational excellence. Our team leverages a diverse range of machine learning methodologies, from gradient-boosting algorithms to state-of-the-art transformer-based architectures. These models process complex data streams to deliver accurate and efficient product classifications at scale. Our ambitious vision is to achieve 100% automation with 0% risk, eliminating manual intervention while ensuring the highest levels of precision and scalability. As a research-focused team, we are dedicated to experimentation and innovation. By integrating multiple data modalities—images, text-based attributes, and categorical data—we aim to develop robust and reliable multimodal models that push the boundaries of what is possible in automated product classification. Through rigorous testing, we continuously refine our approaches to enhance accuracy and adapt to the dynamic needs of a global marketplace. Our commitment to advancing the field extends beyond practical applications. We actively contribute to the scientific community by publishing research in leading conferences such as ICML, NeurIPS, and other Amazon-hosted events. By sharing our findings, we aim to validate the novelty of our methods, inspire the broader community, and remain at the forefront of machine learning innovation. If you’re passionate about innovation, thrive in a dynamic environment, and want to leave your mark on the future of e-commerce, we want to hear from you. Join us in transforming the way the world works with machine learning. This is your opportunity to collaborate with world-class scientists and engineers, advancing innovation in machine learning, natural language processing, and statistical modeling. Your contributions will extend beyond technology—transforming research into scalable solutions that redefine customer experience and operational efficiency. Whether publishing scientific papers, developing patents, or presenting your work to stakeholders, you’ll have a platform to showcase your expertise while driving real-world impact. Key Responsibilities: Conduct pioneering research in machine learning, statistics, and multimodal systems to create groundbreaking solutions for customer and operational challenges. Develop high-performance, production-ready code optimized for large-scale, high-traffic applications. Analyze vast datasets to uncover actionable insights, design scalable algorithms, and seize opportunities for innovation. Validate machine-learning models through rigorous statistical experiments involving millions of users. Partner with software engineering teams to prototype and integrate successful models into global production systems. Collaborate with a multidisciplinary team of applied scientists and engineers to push the boundaries of innovation. Publish research papers at Amazon-hosted and external conferences (e.g., ICML, NeurIPS) to validate and showcase the novelty of the methods developed. About the Location: This role is based in Luxembourg, home to Amazon’s European headquarters. Nestled in the heart of Europe and bordered by France, Belgium, and Germany, Luxembourg is a vibrant, multicultural hub offering a high standard of living and easy access to major European cities. Known for its thriving financial and tech sectors, Luxembourg combines innovation with a rich cultural scene and stunning natural landscapes. Discover more about life in Luxembourg at Promote Luxembourg. Key job responsibilities Drive innovation in machine learning by designing and developing cutting-edge models to accurately classify products on Amazon’s global platform. Harness the power of multimodal approaches, combining text, images, and categorical data, along with advanced transformer architectures and computer vision techniques, to deliver state-of-the-art solutions that set new standards for accuracy, scalability, and efficiency. Collaborate closely with Software Development Engineers to seamlessly integrate machine learning models into production systems, ensuring they meet the stringent latency requirements necessary to process millions of events per day. Address engineering challenges by developing innovative workarounds and optimizations, enabling the efficient deployment and scalability of these models in real-world environments. Contribute to the scientific community by authoring groundbreaking research papers that tackle complex business problems in product classification. Showcase the novelty and practical applications of your methods at renowned conferences such as ICML, NeurIPS, and CVPR, driving forward both the academic and industrial applications of machine learning. A day in the life A Day in the Life of an Applied Scientist II at Amazon As an Applied Scientist II at Amazon, each day is a blend of technical challenges, innovation, and collaboration. Here’s a glimpse into a typical day: 8:30 AM – Start the Day with Focus The day begins with a quick check-in on emails, project updates, and pending code reviews. Any blockers flagged by the team are prioritized to keep progress smooth. 9:00 AM – Team Stand-Up Join the daily stand-up meeting with scientists, engineers, and stakeholders. Share updates on experiments, discuss challenges, and align on priorities. The collaborative environment ensures everyone is on the same page. 10:00 AM – Deep Dive into Research Dive into ongoing research tasks. This could mean reading the latest papers on transformers or multimodal learning, brainstorming ways to integrate new ideas into Amazon’s systems, or designing experiments to test novel approaches. 12:00 PM – Lunch and Networking Take a break to recharge. Amazon offers opportunities to connect with peers over lunch, whether it’s discussing industry trends or exploring ideas for cross-team collaboration. 1:00 PM – Model Development and Experimentation Head into coding and experimentation mode. You might be refining a multimodal model for product classification, training transformers on massive datasets, or fine-tuning a computer vision model to improve prediction accuracy. Metrics are monitored closely to ensure models meet Amazon's high standards. 3:30 PM – Collaboration Time Meet with engineers to optimize deployment strategies for models in production. Discussions often focus on meeting latency requirements for real-time inference without compromising accuracy. This synergy between science and engineering is key to delivering scalable solutions. 4:30 PM – Results Review and Insights Analyze experimental results, document findings, and prepare for upcoming iterations. Insights gained here shape the next steps, whether it’s tweaking hyperparameters or exploring a new approach entirely. 5:30 PM – Wrap-Up Before wrapping up, check in with teammates on Slack or work on updating project documentation. Reflect on the day’s achievements and prepare a to-do list for tomorrow. 6:00 PM – Personal Development and Learning Amazon values continuous learning, so evenings might include taking a course, attending a tech talk, or participating in a knowledge-sharing session within the team. As an Applied Scientist II at Amazon, no two days are exactly the same, but every day is filled with opportunities to solve challenging problems, contribute to impactful projects, and grow as a scientist.

Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to lead key initiatives in robotic intelligence. As a Senior Applied Scientist, you'll spearhead the development of breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, science understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for specific research initiatives, ensuring robust performance in production environments - Mentor and support fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus A day in the life - Develop and implement novel foundation model architectures, working hands-on with our extensive compute infrastructure - Guide and support fellow scientists in solving complex technical challenges, from sim2real transfer to efficient multi-task learning - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster - Mentor team members while maintaining significant hands-on contribution to technical solutions Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through highly innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Are you looking for an opportunity to own a large-scale technology problem? Do you enjoy finding patterns and pushing the boundaries of current possibilities? Are you interested in building reliable and scalable systems that support Amazon's growth? If so, Amazon Devices and Services Finance Technology (FinTech) is the perfect place for you! ABOUT THE TEAM Amazon Devices and Services FinTech is the global team that designs and builds the financial planning and analysis tools for a wide variety of Devices` new and established organizations. From Kindle to Ring and even new and exciting companies like Kuiper (our new interstellar satellite play), this team enjoys a wide variety of complex and interesting problem spaces. They are almost like FinTech consultants embedded in Amazon. ABOUT THIS ROLE The Amazon Devices and Services FinTech team is expanding our data science team that is building a forecasting solution for the Amazon Devices and Services Finance organization, and we are looking for a Data Scientist to join us. As a data scientist, you will dive deep into data from across Amazon's finance organization, extract new insights, drive investigations and algorithm development, and interface with technical and non-technical customers. You will leverage your data science expertise and communication skills to pivot between delivering science solutions, translating knowledge of finance and operational processes into forecasting models, and communicating insights and recommendations to audiences of varying levels of technical sophistication in support of specific business questions, root cause analysis, planning, and innovation for the future. Key job responsibilities - Create various forecasts, including but not limited to Operational Expenses, and drive adoption of these forecasts by various teams within Amazon for financial and operations planning - Continuously innovate through research and the application of the latest machine learning techniques to drive forecasting accuracy improvement - Perform exploratory data analysis to identify business opportunities and develop a plan to address them - Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations - Build customer-facing reporting tools to provide insights and metrics which track forecast performance and explain variance - Utilize code (Python, R, Scala, SQL, etc.) for analyzing data and building statistical and machine/deep learning models A day in the life In a typical day as a data scientist at Amazon FinTech, you'll begin by delving into complex datasets, applying your technical expertise in feature engineering and exploratory data analysis to uncover valuable insights. You'll utilize both traditional time series forecasting techniques as well as more advanced machine learning algorithms to build accurate and reliable forecasting models that solve complex business problems like Operational Expense (OpEx) Forecasting. Collaboration with business, engineering, and partner teams is essential, as you'll translate your data-driven forecasts into actionable insights that align with strategic goals. Throughout the day, you'll innovate by adapting new forecasting methods, ensuring your solutions are stable, scalable, and fault-tolerant. Your strong communication skills and attention to detail will help you manage and integrate large datasets, solve unstructured problems, and drive projects to completion in a fast-paced, dynamic environment. Join us and be a part of our dynamic team, driving the future of financial technology at Amazon.

Alexa Daily Essentials is hiring an Applied Scientist to research and implement large language model innovations to enhance Alexa's language understanding, knowledge representation, reasoning and generation capabilities. The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. We drive over 40 billion+ actions annually across 60 million+ monthly customers, who engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. Our upcoming launches are at the forefront of innovation, delivering step-function improvements in experiences that stretch across the customer journey, and new AI technologies that will enable customers to send Alexa information for future recall and conversation. We collaborate closely with partners such as Amazon.com, Whole Foods, Spotify, CNN, Fox, NPR, BBC, Discovery, and Food Network to deliver our vision. If you are passionate about redefining the personal assistant experience and leveraging innovative technology to improve daily life, we’d love to hear from you. This is an opportunity to make a tangible impact at the heart of the Alexa ecosystem. As an applied scientist, you will advance state of the art techniques in ML and LLM, and work closely with product and engineering teams to build the next generation of the Alexa smart assistant. Key job responsibilities - Rapidly prototype ML/LLM solutions, evaluate feasibility, and drive projects to production deployment - Continuously monitor and improve model performance through retraining, parameter tuning, and architecture refinements - Develop new training and inference techniques to improve model performance - Work cross-functionally across engineering, product, and business teams to understand customer needs, scope science work, and drive science solutions from conception to customer delivery - Research and develop LLM innovations, and lead paper publications. - Code proficiently in Python (required) and Java (preferred); optimize systems for high performance at scale; contribute code directly into production services - Innovate and develop science and engineering solutions that optimize team operations and increase team effectiveness. - Clearly communicate complex technical concepts to non-technical stakeholders and leadership

We're excited to build Large Language Model-based solutions that employ Applied Science, Linguistics, and Software solutions to delight Alexa Smart Home customers! Does this sound exciting to you? Come join us! The Smart Home Science team is focused on making Alexa the user interface for the home. From simple voice commands like turning on lights or adjusting the heat to complex use cases involving home security, entertainment, and environmental control, we are evolving Alexa into an intelligent, indispensable companion. Our goal is to automate daily routines, simplify interactions with appliances and electronics, and provide alerts when something unusual is detected. You can be part of a team delivering features that are highly anticipated by media and well-received by our customers. As a Senior Data Scientist, you will collaborate with scientists, linguists, and software developers to build the next generation of Large Language Model-based Smart Home voice control. You will lead complex data analyses and define, develop, and present research on advanced model- and heuristic-based tools and metrics to analyze the customer experience. Additionally, you'll have the satisfaction of working on a product that Amazon customers, friends, and family use every day! Key job responsibilities - Perform and standardize complex data analyses to provide insights into the customer experience and large language model performance. - Define, develop, and present research on advanced model- and heuristic-based tools and metrics to analyze the customer experience. - Prepare and present data reports to Alexa Smart Home leadership. - Mentor junior scientists to enhance their skills, knowledge, and professional effectiveness. About the team Alexa Smart Home Science is a team of Scientists, Machine Learning Engineers, Linguists, and Software Developers that collaborate together to enable customers to engage with Alexa in a natural, delightful, and frustration-free manner to control device-related experiences in their homes and create the smart home of the future!

Amazon.com is broadly recognized as a leader in providing exceptional Customer Service globally. At Amazon, we are driven by innovation and customer obsession, which is deeply ingrained in everything we do, especially in the Customer Engagement Technology (CET) department. Leveraging conversational Artificial Intelligence (AI) and machine learning (ML) technology, we strive to predict and resolve customer issues through self-service and automation solutions. The CET team leads AI and Large Language Models (LLM)-driven customer experience transformation using task-oriented dialogue systems. We develop multi-modal, multi-turn, goal-oriented dialog systems that can handle customer issues at Amazon scale across multiple languages. These systems are designed to adapt to changing company policies and invoke correct Application Programming Interfaces (APIs) to automate solutions to customer problems. Additionally, we enhance associate productivity through response/action recommendation, summarization to capture conversation context succinctly, retrieving precise information from documents to provide useful information to the agent, and machine translation to facilitate smoother conversations when the customer and agent speak different languages. Key focus areas include: - Task-Oriented Dialog Systems: Building reliable, scalable, and adaptive LLM-based agents for understanding intents, determining eligibilities, making API calls, confirming outcomes, and exploring alternatives across hundreds of customer service intents, while adapting to changing policies. - Lifelong Learning: Researching continuous learning approaches for injecting new domain knowledge while retaining the model's foundational abilities and preventing catastrophic forgetting. - Agentic Systems: Developing a modular agentic framework to handle multi domain conversations through appropriate system abstractions. - Complex Multi-turn Instruction Following: Identifying approaches to guarantee compliance with instructions that specify standard operating procedures for handling multi-turn complex scenarios. - Inference-Time Adaptability: Researching inference-time scaling methods and improving in-context learning abilities of custom models to enable real-time adaptability to new features, actions, or bug fixes without solely relying on retraining. - Context Adherence: Exploring methods to ground responses in specific customer attributes, account information, and behavioral data to prevent hallucinations and ensure high-fidelity responses. - Policy Grounding: Investigating techniques to align bot behavior with evolving company policies by grounding on complex, unstructured policy documents, ensuring consistent and compliant actions. - End-to-End Dialog Policy Optimization: Researching alignment approaches to optimize successful dialog completions. - Scalable Evaluations: Developing automated approaches to evaluate the quality of experience, and correctness of agentic resolutions. Key job responsibilities - Research and development of LLM-based chatbots and conversational AI systems for customer service applications. - Design and implement state-of-the-art Natural Language Processing (NLP) and ML models for tasks such as language understanding, dialogue management, and response generation. - Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate LLM-based solutions into Amazon's customer service platforms. - Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of the chatbots. - Conduct experiments and evaluations to measure the performance of the developed models and systems, and identify areas for improvement. - Stay up-to-date with the latest advancements in NLP, LLMs, and conversational AI, and explore opportunities to incorporate new techniques and technologies into Amazon's customer service solutions. - Collaborate with internal and external research communities, participate in conferences, and contribute to publications to advance the field. A day in the life We thrive on solving challenging problems to innovate for our customers. By pushing the boundaries of technology, we create unparalleled experiences that enable us to rapidly adapt to a dynamic environment. Our decisions are guided by data, and we collaborate with engineering, science, and product teams to foster an innovative and learning environment. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Benefits Summary: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan About the team Join our team of scientists and engineers who develop and deploy LLM-based Conversational AI systems to enhance Amazon's customer service experience and effectiveness. We work on innovative solutions that help customers solve their issues and get their questions answered efficiently, and on associate-facing products that support our customer service associate workforce.

Amazon.com is broadly recognized as a leader in providing exceptional Customer Service globally. At Amazon, we are driven by innovation and customer obsession, which is deeply ingrained in everything we do, especially in the Customer Engagement Technology (CET) department. Leveraging conversational Artificial Intelligence (AI) and machine learning (ML) technology, we strive to predict and resolve customer issues through self-service and automation solutions. The CET team leads AI and Large Language Models (LLM)-driven customer experience transformation using task-oriented dialogue systems. We develop multi-modal, multi-turn, goal-oriented dialog systems that can handle customer issues at Amazon scale across multiple languages. These systems are designed to adapt to changing company policies and invoke correct Application Programming Interfaces (APIs) to automate solutions to customer problems. Additionally, we enhance associate productivity through response/action recommendation, summarization to capture conversation context succinctly, retrieving precise information from documents to provide useful information to the agent, and machine translation to facilitate smoother conversations when the customer and agent speak different languages. Key focus areas include: - Task-Oriented Dialog Systems: Building reliable, scalable, and adaptive LLM-based agents for understanding intents, determining eligibilities, making API calls, confirming outcomes, and exploring alternatives across hundreds of customer service intents, while adapting to changing policies. - Lifelong Learning: Researching continuous learning approaches for injecting new domain knowledge while retaining the model's foundational abilities and preventing catastrophic forgetting. - Agentic Systems: Developing a modular agentic framework to handle multi domain conversations through appropriate system abstractions. - Complex Multi-turn Instruction Following: Identifying approaches to guarantee compliance with instructions that specify standard operating procedures for handling multi-turn complex scenarios. - Inference-Time Adaptability: Researching inference-time scaling methods and improving in-context learning abilities of custom models to enable real-time adaptability to new features, actions, or bug fixes without solely relying on retraining. - Context Adherence: Exploring methods to ground responses in specific customer attributes, account information, and behavioral data to prevent hallucinations and ensure high-fidelity responses. - Policy Grounding: Investigating techniques to align bot behavior with evolving company policies by grounding on complex, unstructured policy documents, ensuring consistent and compliant actions. - End-to-End Dialog Policy Optimization: Researching alignment approaches to optimize successful dialog completions. - Scalable Evaluations: Developing automated approaches to evaluate the quality of experience, and correctness of agentic resolutions. Key job responsibilities - Research and development of LLM-based chatbots and conversational AI systems for customer service applications. - Design and implement state-of-the-art Natural Language Processing (NLP) and ML models for tasks such as language understanding, dialogue management, and response generation. - Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate LLM-based solutions into Amazon's customer service platforms. - Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of the chatbots. - Conduct experiments and evaluations to measure the performance of the developed models and systems, and identify areas for improvement. - Stay up-to-date with the latest advancements in NLP, LLMs, and conversational AI, and explore opportunities to incorporate new techniques and technologies into Amazon's customer service solutions. - Collaborate with internal and external research communities, participate in conferences, and contribute to publications to advance the field. A day in the life We thrive on solving challenging problems to innovate for our customers. By pushing the boundaries of technology, we create unparalleled experiences that enable us to rapidly adapt to a dynamic environment. Our decisions are guided by data, and we collaborate with engineering, science, and product teams to foster an innovative and learning environment. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Benefits Summary: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan About the team Join our team of scientists and engineers who develop and deploy LLM-based Conversational AI systems to enhance Amazon's customer service experience and effectiveness. We work on innovative solutions that help customers solve their issues and get their questions answered efficiently, and on associate-facing products that support our customer service associate workforce.

The EU Amazon Vendor Services (AVS) and WW Vendor Experience (VX) Program teams are looking for an experienced Applied Scientist (L6) to lead advanced causal inference and econometric modeling efforts that will drive critical business decisions and enhance our vendor experience. Amazon strives to be Earth's most customer-centric company, where customers can find and discover anything they might want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce website. Core to Amazon's mission to delight and serve customers is a need to invent on behalf of vendors. The EU AVS program aims to provide an industry-leading account management service at the optimal cost-to-serve for Amazon that exceeds vendors' expectations and expedites their growth on Amazon. The WW VX program vision is to make Amazon the most preferred, trusted, and efficient distribution option for vendors by building an industry-leading experience for every vendor across all global touchpoints. Both AVS and VX are core inputs to improving the end Customer Experience and Amazon's Long-Term Free Cash Flow. The AVS and VX program teams are diverse organizations with employees across Europe and with partner teams around the globe. This role can be based in London, Paris, Madrid, or Luxembourg. These teams drive improvements in products, services, tools, processes, communication, and vendor education world-wide working with partner teams in Europe, North America, Japan, and emerging locales and are responsible for all elements of a vendor's interaction with Amazon including listing, catalog management, ordering, supply chain, marketing, payments, value-added services, and vendor support. As a senior member of our data and analytics (DNA) team, you will play a crucial role in developing and implementing sophisticated causal inference models and econometric analyses to drive data-informed decisions across our organization. You will work closely with product managers, data scientists, and business stakeholders to deliver impactful insights that shape our vendor strategies and optimize our operations. Key job responsibilities - Develop advanced econometric and statistical models to rigorously evaluate the causal incremental impact of product feature releases. - Develop approaches to understand the causal dependency between various business performance metrics. - Estimate the incremental impact of actions designed to reduce vendor cost to serve. - Own the end-to-end development of novel causal inference models that address the most pressing needs of our business stakeholders and help guide their future actions. - Collaborate cross-functionally with marketing, product, data science, and engineering teams to define the measurement strategy and ensure alignment on objectives. - Work with BIEs, data scientists, and product managers to automate models in production environments. - Stay up-to-date with the latest research and methodological advancements in causal inference, causal ML, and experiment design to continuously enhance the team's capabilities. - Effectively communicate analysis findings, recommendations, and their business implications to key stakeholders, including senior leadership. - Mentor and guide colleagues, fostering a culture of analytical excellence and innovation.