Careers

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.

Amazon Ads delivers advertising experiences across Amazon's owned-and-operated properties and third-party networks, reaching hundreds of millions of customers worldwide. Within Amazon Ads, Advertising Trust is the science-first organization responsible for ensuring every ad shown to customers meets Amazon's content policies — at massive scale, across all ad formats and global marketplaces. The Ads Trust Science team builds the ML systems that automate content moderation decisions: multimodal classification, retrieval-based labeling, LLM reasoning, and agentic self-improvement architectures. This requires inventing new approaches at the intersection of computer vision, NLP, information retrieval, and generative AI. We are seeking an Applied Science Manager to lead a team of applied scientists building next-generation content moderation intelligence. You will own the science roadmap for one of the highest-impact automation programs in Amazon Advertising, defining how multimodal content understanding, retrieval-first classification, and LLM-based reasoning combine into a production system that serves global advertising at scale. Key job responsibilities * Lead a team of applied scientists working across multimodal ML (vision-language models, video understanding), large-scale retrieval systems (embedding-based similarity and deduplication), and generative AI (LLM-based policy reasoning, knowledge distillation, agentic architectures, reinforcement learning). * Define the science strategy for ads trust. * Own end-to-end delivery of ML solutions: problem formulation, offline experimentation, online A/B testing, and production deployment. Your models directly move automation and defect metrics reported to senior leadership. * Build and grow scientists — hire, mentor, and develop team members. Raise the science bar through structured review processes and a publication culture within Amazon. * Partner with engineering, product, and operations teams to translate science investments into measurable automation improvements. Influence roadmaps across dependent teams. * Communicate science strategy and results to senior leadership through narratives, technical deep-dives, and roadmap documents.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Science Manager with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to lead a team ensuring the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Science Manager will lead and mentor a team of Applied Scientists who develop comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. The manager will guide the team in designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that align with core scientist team developing Amazon Nova models. The Applied Science Manager will oversee expert-level manual audits, meta-audits to evaluate auditor performance, and provide coaching to uplift overall quality capabilities across the team. A critical aspect of this role involves managing the development and maintenance of LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Science Manager will also oversee the configuration of data collection workflows and ensure effective communication of quality feedback to stakeholders. The manager will have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. The Applied Science Manager will be responsible for recruiting, hiring, and developing team members, conducting performance reviews, setting clear expectations and growth plans, and fostering a culture of scientific excellence and innovation. The manager will communicate with senior leadership, cross-functional technical teams, and customers to collect requirements, describe product features and technical designs, and articulate product strategy. A day in the life An Applied Science Manager with the AGI team will lead quality solution design, guide root cause analysis on data quality issues, drive research into new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. The manager will work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. The manager will also conduct regular 1:1s with team members, provide mentorship and coaching, and ensure the team delivers high-impact results aligned with organizational goals.

Amazon.com’s Product Detail Page team is looking for talented, motivated and passionate applied scientist to be part of the design and development of a highly scalable multi-tiered shopping application to provide the best possible online shopping experience for Amazon customers world-wide. Our team is comprised of talented applied scientists, developers, testers, program managers, designers and product managers tasked with the singular goal to create THE world's best buying experience. Scientists on this team develop the next-generation technologies and experiences that change how millions interact and shop online. To provide the best possible online shopping at the scale of the web requires ideas from every area of computer science, including distributed computing, large-scale system design, machine learning, natural language processing, data compression and user interface design; the list goes on and is growing every day. We need our scientists to be versatile and always eager to tackle new problems as we continue to push technology forward. Our team leverages sophisticated econometric, machine learning, and big data technologies to help customers to discover the right products at the right prices from millions of trusted sellers billions of times a day. If you are looking for a career-defining opportunity on one of the most customer centric and business impacting teams within Amazon, we’d love to hear from you. We are looking for a Senior Applied Scientist to help build the next generation of Detail Page optimization algorithms. These new set of algorithms will incorporate the continually changing preferences of our customers and continue to scale with numerous new programs that Amazon is introducing for our customers. You will work with multiple Amazon businesses and programs to identify big business opportunities and propose new business features and technical systems to improve customer experience on Amazon Detail Page, Search Page and many other widgets throughout the website. You will be responsible for the quality of algorithm design and will get the opportunity to present your ideas and share results of your deliverables with Amazon executives on a frequent basis. You will get an opportunity to work with senior scientists to define and enforce broad, company-wide technical standards in optimization techniques, statistical modeling and simulation techniques, and/or data analytics.

Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better? Amazon Robotics is looking for a talented Applied Scientist. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations. Key job responsibilities In this role you will: • Work closely with other scientists and engineers, and be part of Amazon’s diverse global science community. • Publish your research in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise. A day in the life You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale. About the team The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.

Join our Frontier AI & Robotics team to support the development of test infrastructure for next-generation robotic systems that will transform how robots perceive and interact with the world. You'll take ownership of designing and implementing software-driven test and validation frameworks across advanced actuators, precision sensors, and robotic subsystems — ensuring engineering validation and manufacturing test readiness to support breakthrough AI research and real-world deployment. Key job responsibilities - Test Infrastructure Development - Design, develop, and maintain automated test frameworks for engineering validation and manufacturing test of robotic systems and subsystems. Build scalable, reusable test software that integrates with hardware-in-the-loop (HIL) environments, data acquisition systems, and robotic control interfaces. - Software Integration & Automation - Develop test automation software in Python, C++, or equivalent languages to exercise actuators, sensors, vision systems, and communication interfaces. Implement scripted test sequences, data logging pipelines, and pass/fail criteria for prototype and production-intent hardware. - Hardware Bring-Up & SW/HW Integration - Perform hands-on hardware bring-up of robotic subsystems including power sequencing, communication interface validation, peripheral initialization, and firmware/software integration. Debug across the full stack, from board-level hardware through embedded firmware to application-layer test software, to bring new hardware revisions to a validated, testable state. - Engineering Validation & Mfg Test - Create and execute test protocols for functional validation, performance characterization, and regression testing of robotic subsystems. Develop test stations and software tooling that transition from R&D validation through manufacturing test readiness. - Debugging & Failure Analysis - Troubleshoot and root-cause issues across the robotic platform (power, compute, comms, actuators, sensors) using software diagnostic tools, log analysis, and bench instrumentation. Conduct failure analysis from component to system level. Reproduce critical failures and bridge communication between the lab and engineering teams. - Data Analysis & Reporting - Build data pipelines and analysis tools to aggregate test results, identify trends, and generate automated test reports. Develop dashboards or visualization tools that provide engineering teams with actionable insights on hardware quality and reliability. - Technical Documentation - Author and maintain test plans, test procedures, automation framework documentation, failure analysis reports, and troubleshooting guides; uphold consistent documentation standards across the lab. - Lab Operations Support - Support equipment maintenance, inventory management, vendor coordination, and safety/regulatory compliance for test lab environments. A day in the life Your focus centers on the software test infrastructure and hardware integration that validates our advanced robotic platforms. You'll develop and maintain automated test systems for engineering validation and manufacturing test while getting hands-on with hardware bring-up and debugging, working alongside hardware engineers, firmware developers, and fellow technicians. Your responsibilities include writing test automation code, building HIL test environments, bringing up new hardware revisions, debugging SW/HW integration issues at the bench, analyzing test data, and designing test fixtures. Throughout the day, you balance developing new test capabilities with hands-on hardware integration, supporting urgent prototype bring-up requests, maintaining test infrastructure, and preparing test readiness for upcoming milestones. You're switching between writing code at your workstation, probing signals and validating hardware at the bench, collaborating in design reviews with engineers, and ensuring test lab equipment is calibrated and maintained. 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 frontier 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 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.