Careers

Some CX shopping defects might be straightforward to detect and track. The interesting ones are not because they depend on what a customer perceives. For example, a search page may return legitimately different results, yet a shopper has no way to tell apart. We turn these ambiguous perception questions into a measurable artifact using LLMs, and we build the frameworks to know exactly where model judgment can be trusted and where a human must decide and proving it, against ground truth, at Amazon scale. This is one example of an LLM based measurement pipeline you will own, but that’s not all. You will extend that measurement to other parts of the shopping experience like the homepage and the detail page, where the same customer problem looks nothing like it does in search results, and where you will design the measurement from scratch. The larger goal is what makes this role unusual. Teams across Amazon are each independently figuring out how to label quality with LLMs, hitting the same problems alone: prompts that break on the next model version; golden sets nobody audited, accuracy that collapses in other locales. Through the work above, you will set the standard and build the production tooling behind it. Reusable labeling pipelines, evaluation frameworks, and inference infrastructure that hold up against Amazon-sized data and get adopted by teams who did not have to use them. Key job responsibilities - Design and improve LLM-based labeling for perception-driven defects: prompt design, sampling strategy, and the split between model judgment and human annotation. - Validate labeling quality against human ground truth, and build and maintain the golden datasets that make that validation possible. - Extend perceived-duplicate measurement to other parts of the shopping experience, designing the methodology where none exists and evaluating approaches already in use where one does. - Build reusable, production-grade labeling and evaluation tooling, batch inference, quality sampling, prompt and model version control, that operates on Amazon-scale data. - Define and publish the standards other teams adopt for using LLMs to measure customer experience. - Partner with science, engineering, and product teams across Stores to make quality measurement usable in their decisions, and present metric results and methodology changes to stakeholders. A day in the life We are a small team, which means your work is visible and your scope grows as fast as you do. There are existing partnerships with other teams and a paved way to cross org influence.

Every day, hundreds of millions of customers arrive on Amazon's Homepage, search for a product, and land on a detail page. Along the way, some of what we show them is irrelevant — a recommendation that misreads what they want, a widget that repeats what they just saw, a headline that doesn't say what it means, a product that shouldn't have been surfaced at all. We have built the ability to detect these defects at scale using Large Language Models (LLMs), and we report them to Amazon's most senior leadership as a company-level measure of shopping quality. What we have not yet built is the confidence to act on every one of them. That is the problem you will own. Today, the most consequential categories of shopping defects — product quality, duplication, staleness, irrelevance — go largely unenforced. Not because we cannot detect them, but because we have not yet clearly defined when they lead to customer dissatisfaction. These are genuinely hard questions. When is a recommendation irrelevant rather than merely unexpected? When are two products duplicates rather than legitimate alternatives? Every answer carries consequences for customers and advertisers. As Principal Data Scientist, you will own the analytical rigor behind Amazon's store quality metrics end-to-end: how they are defined, how faithfully our LLM implementations execute those definitions, and how accurate the resulting judgments actually are across relevance, presentation, product quality, and duplication. You will go deep enough to inspect individual model judgments and read the edge cases where they break, then come back up to argue definitional questions with the leaders who own the outcomes on both sides. You will also define experiments that will turn judgement into facts backed by data. Your influence will come from deep dives so well-constructed that stakeholders across two large organizations change their minds. Getting there means building the tooling that makes rigor repeatable rather than heroic: agents that walk the store the way a customer would, automated inspection of experiments, and feedback loops that finally connect what customers tell us directly to the metrics we optimize. Much of the customer voice we already collect goes underused today; you will change that. You will begin on the Homepage, where measurement is most mature, then extend the methodology to Detail Page and Search — carrying not just the metrics but the standard of evidence with them. Key job responsibilities - Validate store quality metrics end-to-end: inspect metric definitions, audit LLM implementations, and evaluate LLM judgment accuracy across quality dimensions (relevance, presentation, product quality, duplicates) - Starting with Homepage, extending cross-page — identify gaps and inconsistencies between Organic and Ads treatments that block the tiered enforcement framework - Drive alignment through deep dives that present evidence to both Stores and Ads stakeholders - Build and deliver deep-dive tooling (walk-the-store bots, automated experiment inspection via Gecko, VoC feedback loops) that make metric validation repeatable and self-serve - Own the analytical rigor behind the alignment on definitions of subjective/debated metrics and moving them to aligned/enforceable About the team Core Shopping Data Science owns the measurement of shopping quality across Amazon's Core Shopping eexperiences, including defect metrics reviewed by Amazon's most senior leadership. We focus on the long term and big picture to ensure that the full Amazon shopping experience balances strategic trade-offs. We work across Stores and Advertising, partnering with personalization, ranking, and search teams to turn measurement into changes customers can feel.

The Alexa Edge AI team is seeking a talented and motivated Applied Scientist to join our newly established team in Bangalore. In this role, you will design, develop, and deploy state-of-the-art machine learning models spanning computer vision (CV), audio (including speech) processing, and multimodal semantic understanding for both edge and cloud deployment. You will work at the intersection of multiple modalities to build systems that can perceive, interpret, and reason about the world — pushing the boundaries of what's possible in unified multimodal intelligence. This is a unique opportunity to be a founding member of a brand-new site, shaping the team culture, technical direction, and research agenda from the ground up. Key job responsibilities Model Development: Design and build deep learning models for computer vision, audio understanding, and multimodal semantic fusion — including architectures that enable joint reasoning across visual, auditory, and textual modalities. End-to-End Ownership: Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale. Research & Innovation: Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply SOTA techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL). Mentorship & Culture Building: As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture. A day in the life An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production. About the team The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.

The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. As an Applied Science Manager on the team, you will lead a team of scientists to define and execute a transformative vision for holistic catalog quality measurement, metadata enrichment, and content integrity. Your team will own the science solutions for foundational quality detection frameworks, metadata validation and correction technologies, state-of-the-art algorithms to identify and resolve catalog anomalies (violative content, duplicative/low value content, misattributed tracks, incorrect metadata), and/or agentic AI solutions that help internal teams quickly surface and fix quality issues to ensure customers receive accurate, complete catalog experiences. Key job responsibilities You independently manage a team of scientists. You identify the needs of your team and effectively grow, hire, and promote scientists to maintain a high-performing team. You have a broad understanding of scientific techniques, several of which may fall out of your specific job function. You define the strategic vision for your team. You establish a roadmap and successfully deliver scientific solutions that innovate on catalog quality detection, metadata enrichment, and content integrity. You define clear goals for your team and effectively prioritize, balancing short-term quality improvements and long-term innovation in catalog intelligence. You establish clear and effective metrics and scientific process to enforce consistent, high-quality artifact delivery and measurable catalog quality improvements. You proactively identify risks and bring them to the attention of your manager, customers, and stakeholders with plans for mitigation before they become roadblocks. You know when to escalate. You communicate ideas effectively, both verbally and in writing, to all types of audiences. You author strategic documentation for your team. You communicate issues and options with leaders in such a way that facilitates understanding and that leads to a decision. You work successfully with customers, leaders, and engineering teams. You foster a constructive dialogue, harmonize discordant views, and lead the resolution of contentious issues. About the team We are a team of scientists and MLEs focused on music catalog quality and metadata intelligence. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and data quality systems with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, content operations teams, and other scientists with expertise in music metadata, content classification, and building scalable modeling and software solutions that keep the Amazon Music catalog accurate, complete, and trustworthy.

Amazon’s Marketplace business is one of the largest in the world operating in 23 countries. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. The IN Seller Partner Services Tech organization is responsible for building delightful seller experiences with the vision of being the technology foundation that makes Amazon the #1 choice for sellers of all types and price-sensitive customers in India and emerging markets—transforming how a billion people buy and sell, thereby transforming lives. By making its sellers successful, Amazon can attract and retain sellers on the platform, which in turn offers its customers the best selection, convenience, pricing and experience resulting in multi-billion-dollar business. This organization enables sellers of all stripes across a multitude of business lines existing or new such as Q-Commerce, Pharmacy and Out of country selling. We are looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of our science team, you will provide expertise that helps accelerate the business and make it profitable. You will research, design and improve on the models that will directly impact Amazon’s Selection quality and maximize fee revenue. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. Looking for candidate with expertise in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables

At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our mission is to prevent denied entities from transacting with Amazon businesses. We build automatic mechanisms to detect and prevent prohibited transactions with denied entities using a diverse set of algorithms and machine learning techniques. We screen over a billion events every day and develop algorithms which are able to scale and detect suspicious entities . We are still Day 1 and have an exciting road map to build Machine Learning (ML) and Generative AI (LLM) powered detection and resolution systems to help scale Amazon for years to come. We are seeking an Sr. Applied Scientist to join our team and help tackle challenging problems at the forefront of machine learning and artificial intelligence. Working closely with a multidisciplinary team of engineers, data scientists, and domain experts, you will play a crucial role in defining innovative ML/AI-powered customer experiences and solutions. If you have an entrepreneurial mindset, the technical depth to deliver impactful results, and a passion for innovation, we want to hear from you. Key job responsibilities In this role, you will: • Drive the research, design, and development of novel ML/AI models and systems to power critical products and services • Collaborate cross-functionally to deeply understand business requirements, customer needs, and technical constraints • Rapidly prototype, test, and iterate on ML/AI solutions, iterating quickly based on data and feedback • Communicate complex technical concepts to technical and non-technical stakeholders • Mentor and grow a team of talented ML scientists and engineers • Stay up-to-date on the latest advancements in AI/ML and identify opportunities to apply emerging techniques A day in the life - Starting the day by reviewing the latest model performance metrics and identifying areas for improvement - Brainstorming new architectures and approaches with your cross-functional team during a whiteboard session - Diving deep into a complex dataset, leveraging advanced statistical and AI techniques to uncover hidden insights - Prototyping a new model and running a series of experiments to optimize its performance - Preparing a presentation to pitch your latest research findings and recommendations to product and engineering leaders - Mentoring junior scientists, providing guidance on coding best practices and problem-solving strategies About the team Why Amazon Security At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training and 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, training, and other career-advancing resources here to help you develop into a better-rounded professional.

The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Member of Technical Staff with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Member of Technical Staff with the AGI team, you will support the development of algorithms and modeling techniques, to advance the state of the art with LLMs. You will support the foundational model development in an applied research role, including model training, dataset design, and pre- and post-training optimization. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.

Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, the Amazon Global Supply Chain Optimization Technology (SCOT) organization is for you. Watch this video to learn more about our organization, SCOT: http://bit.ly/amazon-scot We are the Optimal Sourcing Systems team (OSS) within SCOT and are looking for a Data Scientist II to join us! OSS designs and builds systems that measure and manage Amazon’s supplier capabilities, identify and react to supply disruptions, and prioritizes inbound freight for our global network. OSS software is used by every country Amazon services, and is a critical link to ensuring Amazon offers the products our customers want, at the lowest possible cost. This team under OSS orchestrates and tracks inventory movement into Amazon's network, maintains performance feedback loops, and ensures vendor compliance. The Data Scientist II, in partnership with the Product Management, Operations, and Tech teams, will lead efforts in four areas: 1) Building models to set optimal parameters such as lead times to ensure the accuracy of our Inbound network 2) Building analytical frameworks to identify and drive improvements in purchase order lifecycle management and defect coaching/chargebacks 3) Developing Gen AI solutions related to dispute evaluation and vendor coaching 4) Building models and solutions to enable collaborative inventory planning with vendors The ideal candidate thrives in ambiguous problem spaces, relishes working with large volumes of data, and enjoys the challenge of highly complex supply chain contexts. They can translate complex business logic into scalable models and communicate insights effectively to both technical and non-technical stakeholders. Keys to success in this role include exceptional analytics, statistics, judgment, and communication skills. Experience with supply chain optimization, operations research, or vendor management systems is a plus. Key job responsibilities - Collaborate with product managers, science, and engineering teams to design and implement model solutions for Sourcing Execution & Performance systems - Use large datasets or experiments to make causal inferences or predictions - Work with engineers to automate science analysis processes and build scalable measurement solutions - Interpret data, write reports, and make actionable recommendations - Drive technical standards and best practices for the team's Science solutions - Mentor and provide technical guidance to other team members on complex projects

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. We are looking for a Senior Applied Scientist to join our team that is building revolutionary enterprise applications leveraging machine learning generative AI and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers. As a Senior Applied Scientist you will design and develop state-of-the-art machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact-translating scientific breakthroughs into production systems that serve millions of customers. We operate like a startup within AWS offering you the opportunity to tackle unprecedented challenges while working with the latest technologies in deep learning large language models and optimization. If you are passionate about pushing the boundaries of applied science thrive in ambiguous problem spaces and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources we want to hear from you. Key job responsibilities • Design develop and deploy novel machine learning models for demand forecasting inventory optimization anomaly detection and supply chain decision-making. • Lead the development of GenAI and Agentic AI solutions that automate complex supply chain workflows and deliver intelligent adaptive recommendations to customers. • Formulate real-world business problems as machine learning problems; define data requirements model architectures evaluation metrics and experimentation frameworks. • Drive end-to-end applied science projects from ideation through experimentation offline evaluation A/B testing and production deployment at scale. • Publish research findings in top-tier conferences (NeurIPS ICML KDD AAAI) and file patents to advance Amazon's intellectual property. • Mentor and develop junior scientists; raise the technical bar for the science team through code reviews design reviews and knowledge sharing. • Collaborate closely with engineering product management and business stakeholders to translate scientific capabilities into customer-facing product features. • Influence the technical strategy and scientific roadmap for the organization; identify new areas of investment and emerging opportunities in AI/ML. • Establish and promote best practices for experimentation model validation and responsible AI development across the team. About the team Amazon 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. 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. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.

Are you passionate about using science to make the digital world more secure? The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Scientist III to invent and build ML-driven systems that fundamentally change how Amazon identifies, prioritizes, and mitigates application security risk at scale. Our team sits at the intersection of data science, machine learning, and security operations. We build the intelligence layer that powers Amazon's application security programs: risk-scoring models that rank tens of thousands of applications, graph-based systems that map security context across architectures, and analytics platforms that drive data-informed decisions for security leadership. This is science with direct, measurable impact on Amazon's security posture. As an Applied Scientist III, you will lead the invention and delivery of novel ML solutions for complex, ambiguous problems in the security domain. You will work with large-scale datasets spanning application metadata, code signals, vulnerability findings, and organizational context to develop models that help Amazon focus security resources where they matter most. Key job responsibilities - Lead the design, development, and deployment of ML models and scientific solutions for application security prioritization, complexity scoring, and risk assessment - Frame ambiguous security problems into well-defined scientific challenges, proposing novel approaches when existing methodologies are insufficient - Architect and implement production-grade ML pipelines (feature extraction, model training, scoring, deployment) on AWS services (S3, Glue, SageMaker, Neptune) - Develop and extend graph-based models that capture security-relevant relationships between applications, services, teams, and vulnerabilities - Drive the team's scientific agenda by proposing new research initiatives, conducting experiments, and iterating on models using rigorous evaluation methodologies - Partner with security engineers, data engineers, and TPMs to translate model outputs into actionable intelligence for security review programs - Establish and raise the bar for scientific rigor: peer review code and designs, set best practices for experimentation, and document findings for reproducibility - Publish results internally and externally at peer-reviewed venues when appropriate About the team The Data Analytics & Engineering (DNA) team is a small, high-impact group within Amazon's Application Security organization. We build ML models, graph-based systems, and analytics platforms that determine how Amazon prioritizes security coverage across tens of thousands of applications. We're a hybrid team of scientists, data engineers, and security engineers who ship production science, embrace ambiguity, and operate with high ownership. If you want meaningful work that protects customers at Amazon's scale, this is the team. Diverse Experiences Amazon Security 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.