Careers

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 independently 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 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 unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic 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 unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Enable unprecedented robustness and reliability, industry-ready - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities 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 As an Applied Science Manager in the Foundations Model team, you will: - Build and lead a team of scientists and developers responsible for foundation model development - Define the right ‘FM recipe’ to reach industry ready solutions - Define the right strategy to ensure fast and efficient development, combining state of the art methods, research and engineering. - Lead Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Lead Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Lead Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Lead Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.

We're seeking an Applied Scientist to lead and innovate in applying advanced AI technologies that will reshape how businesses sell on Amazon. Our team is passionate about leveraging Machine Learning, GenAI, and Agentic AI to help B2B sellers optimize their operations and drive growth. Join Amazon Business 3P (Third Party - Sellers) - a rapidly growing global organization where we innovate at the intersection of AI technology and B2B commerce. We're reimagining how sellers reach and serve business customers, creating intelligent solutions that help them grow their B2B business on Amazon. From AI-powered Seller Central tools to smart business certifications, dynamic pricing capabilities, and advanced analytics, we're transforming how B2B selling happens. As an Applied Scientist II on our AB 3P Tech team, you'll drive the development and implementation of state-of-the-art algorithms and models for supervised fine-tuning and reinforcement learning. You'll work with highly technical, entrepreneurial teams to: - Design and implement AI models that power the B2B selling experience - Lead the development of GenAI products that can handle Amazon-scale use cases - Drive research and implementation of advanced algorithms for human feedback and complex reasoning - Make strategic AI technology decisions and mentor technical talent - Own critical AI systems spanning from Seller Central to Amazon Business detail pages Join us in shaping the future of B2B selling - we're building applied AI solutions that businesses love and trust for their day-to-day success. If you are scrappy and bias for action is your favorite Leadership Principle, you'll fit right in as we innovate across the seller experience to create significant impact in this fast-growing business. Key job responsibilities Key job responsibilities: - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in Gen AI - 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 Gen AI 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 About the team At Amazon Business Third Party (AB3P) Tech, we're revolutionizing B2B e-commerce by empowering sellers in the business marketplace. Our scope spans the complete B2B selling journey, from Seller Central to Amazon Business detail pages, cart, and checkout for merchant-fulfilled offers. Our entrepreneurial culture and global reach define us. We develop features across seller experience, delivery, certifications, fees, registration, and analytics, collaborating with worldwide teams and leveraging advanced AI technologies to continuously innovate. Working in true Day 1 spirit, we build next-generation solutions that shape the future of B2B commerce. Join us in building next-generation solutions that shape the future of B2B commerce.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities • Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges • Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production • Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization • Provide customer and market feedback to product and engineering teams to help define product direction

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 catalog, music content understanding, ranking, sequencing and recommendation 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 AI/ML to shape Amazon Music experiences for millions. Develop state of the art models leveraging and advancing the latest developments in machine learning and genAI. 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.

Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!

Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that safeguard Amazon's customers and legitimate sellers while ensuring a fair and trusted shopping experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Risk Intelligence Science Team. We're seeking an exceptional scientist who can revolutionize how we protect our marketplace through intelligent automation. As a key member of our team, you'll develop and deploy state-of-the-art machine learning systems that analyze millions of seller interactions daily, ensuring the integrity and trustworthiness of Amazon's marketplace while scaling our operations to new heights. Your work will directly impact the safety and security of the shopping experience for hundreds of millions of customers worldwide, while supporting the growth of honest entrepreneurs and businesses. Key job responsibilities • Use machine learning and statistical techniques to create scalable abuse detection solutions that identify fraudulent seller behavior, identity change, and marketplace manipulation schemes • Innovate with the latest GenAI technology to build highly automated solutions for efficient seller verification, transaction monitoring, and risk assessment • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to prevent and detect sophisticated abuse patterns across the marketplace • Learn, explore and experiment with the latest machine learning advancements to protect customer trust and maintain marketplace integrity while supporting legitimate selling partners • Collaborate with cross-functional teams to develop comprehensive risk models that can adapt to evolving abuse patterns and emerging threats About the team You'll be working closely with business partners, science 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.

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.

Are you interested in machine learning, deep learning, automated reasoning or optimization? If so, we want to hear from you! We are looking for scientists with interest in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for coding agents. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems for coding agents. You’ll have the opportunity to write technical white papers, create technical roadmaps and drive production level projects that will support Amazon Science. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. A day in the life About the team Agentic AI Agentic AI drives innovation at the forefront of artificial intelligence, enabling customers to transform their businesses through cutting-edge AI solutions. We build and deliver the foundational AI services that power the future of cloud computing, helping organizations harness the potential of AI to solve their most complex challenges. Join our dynamic team of AI/ML practitioners, applied scientists, software engineers, and solution architects who work backwards from customer needs to create groundbreaking technologies. If you're passionate about shaping the future of AI while making a meaningful impact for customers worldwide, we want to hear from you.

AWS Deep Science for Systems & Services is looking for a Sr. Manager, Applied Science who will lead a team of world class scientists to work on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS Deep Science for Systems & ServicesL you lead a team inventing, implementing, and deploying state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions, interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: - Hardware-informed efficient model architecture, training objective and curriculum design - Distributed training, accelerated optimization methods - Continual learning, multi-task/meta learning - Reasoning, interactive learning, reinforcement learning - Robustness, privacy, model watermarking - Model compression, distillation, pruning, sparsification, quantization