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Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! The Prime Video Title Lifecycle Presentation team sits at the intersection of science, experimentation, and customer experience. We leverage data signals and rigorous testing to present the most engaging information about our content to customers at precisely the right moment. Our mission is to ensure every customer interaction with Prime Video content is informed, relevant, and compelling in order to drive discovery and engagement across our vast catalog. We're seeking a Sr. Applied Scientist who excels at building sophisticated machine learning systems for content presentation and discovery. The ideal candidate brings deep expertise in: - Multi-modal embeddings for rich metadata representation, enabling nuanced understanding of content attributes and customer preferences - Contextualized ranking systems that adapt to customer intent, viewing context, and real-time signals - Reinforcement learning frameworks that create continuous improvement loops, allowing our systems to learn and optimize from customer interactions over time - General modeling techniques with strong fundamentals in machine learning and statistical methods - Recommender systems experience, with proven ability to build and scale personalization solutions You'll work with technology to solve complex problems in content discovery, leveraging large-scale data to create experiences that delight millions of Prime Video customers worldwide. Key job responsibilities - Lead Cross-Functional Science Initiatives: Drive a diverse portfolio of applied science projects spanning recommender systems, generative AI agent development and evaluation across multiple modalities, and computer vision applications. Demonstrate both breadth of understanding across technical domains and sufficient depth in each area to effectively lead multiple concurrent initiatives to successful outcomes. - Bridge Science and Engineering for Production-Scale Deployment: Partner with engineering teams to productionize machine learning models at Prime Video scale. Develop production-ready science code that meets engineering standards for performance, reliability, and maintainability, ensuring seamless transition from research to deployment. - Mentor and Develop Technical Talent: Provide technical mentorship and guidance to junior scientists and engineers on applied science methodologies, best practices, and professional development. Foster a culture of scientific rigor and continuous learning within the team.

Are you a passionate scientist who wants to build AI agents that make a real difference in people's lives? At Ring, our mission is to make neighborhoods safer, and we believe agentic AI will change how customers interact with their homes and communities. You'll invent agents that reason about real-world situations, take meaningful action, and keep customers in control, and then you'll see them reach millions of households. As an Applied Scientist, you'll work with talented peers to push the frontier of agentic AI. You'll build agents that turn the multimodal signals captured by Ring devices into understanding and action. You'll tackle open problems in planning, tool use, learning from feedback, and reliability, taking ideas from research all the way to deployment at scale. You'll collaborate with teams across Amazon to advance the science of customer experiences through highly optimized, integrated hardware and software platforms. Key job responsibilities - Design, develop, and deploy LLM-based agents that plan and carry out multi-step tasks for customers using tools, services, and device data. - Advance the state of the art in agent capabilities such as planning, tool use, memory, and learning from feedback (e.g., RL and agent fine-tuning), and publish where appropriate. - Partner with engineering, product, and science teams to turn research into agent-driven experiences that help keep homes and neighborhoods safer.

We are seeking a Senior Applied Scientist to lead research and development of novel security validation and monitoring techniques for AI systems at scale. You will own and contribute to four critical workstreams: 1. Real-Time Agent Monitoring Design and implement scientific approaches for continuous behavioral analysis of AI agents in production—detecting anomalous actions, prompt injection exploitation, and policy violations in real time. 2. MCP Server Validation Develop novel validation frameworks to assess the security posture of Model Context Protocol (MCP) servers, including input sanitization verification, tool-use authorization boundaries, and data exfiltration detection. 3. AI-Enabled Application Validation Invent and deliver scalable methodologies for security testing of AI-enabled applications, including adversarial robustness evaluation, safety guardrail bypass detection, and trust boundary verification. 4. AI Asset Discovery & Inventory Research and build scalable techniques to automatically discover, identify, and catalog all AI-enabled applications and services across the company—maintaining a comprehensive, continuously updated database of AI assets. Key job responsibilities Invent • Identify and frame new research challenges in AI security where problems are ill-defined and require novel scientific paradigms at the product level. • Drive the team's scientific agenda for agent monitoring, validation research, and AI asset discovery; propose new initiatives and secure leadership buy-in. • Publish research results at peer-reviewed internal and external venues (e.g., USENIX Security, IEEE S&P, NeurIPS, ICML security workshops) when appropriate. • Articulate key scientific challenges of current and future AI security threats and present interventions to address them. • Make trade-offs between short-term tactical security needs and long-term research investments. Implement • Lead the design, implementation, and successful delivery of scientifically complex security solutions into production—both brand new systems and evolutions of existing ones. • Write significant portions of critical-path code for detection models, validation engines, and asset discovery / classification systems. • Independently assess and select appropriate technologies (e.g., streaming inference frameworks, graph-based anomaly detection, NLP-based service classification, code/traffic analysis for AI fingerprinting) for production systems. • Drive adoption of best practices in scientific methodology and software engineering across the team; provide insightful peer reviews of code, design, and architecture artifacts. • Deliver solutions that are inventive, maintainable, scalable, and extensible. Influence • Autonomously drive discussions with security engineers, product managers, and scientist peers across multiple teams. • Build consensus on larger cross-team security initiatives and factor complex efforts into independent workstreams. • Proactively identify and resolve endemic problems, including areas where current security tooling limits innovation of partner teams. • Actively recruit, mentor, and develop other scientists; provide technical assessments for promotions. • Contribute to the broader internal and external scientific communities as a subject matter expert in AI security. About 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's Community Intelligence Science & Engineering team operates at the intersection of computational social science, artificial intelligence, and operational excellence. We analyze how Amazon's presence impacts local communities and embed those insights into core operational models. You'll work at the forefront of applied AI and causal inference, building systems that influence decisions affecting thousands of communities daily. This role offers a unique opportunity to shape how the world's most customer-centric company builds sustainable relationships with the communities we serve. A successful Data Scientist on our team demonstrates exceptional scientific rigor combined with pragmatic execution. You'll balance breakthrough research with production deployment, collaborate across multiple science organizations, and translate complex analytical findings into actionable insights that influence senior leadership strategy. As a Senior Data Scientist, you will partner with product teams, scientists, and technical leaders across Last Mile, Middle Mile, WW Ops Security, WW Real Estate, and other organizations to identify opportunities where community intelligence creates multiplicative business impact. Key job responsibilities - Design and deploy production ML forecasting systems with extended prediction horizons using multi-modal data sources - Build and scale AI-powered analytics platforms that process large volumes of unstructured data from diverse sources including social media, traditional media, and internal systems - Collaborate with operational science teams to integrate analytical insights and predictive signals into existing operational models and decision-making systems - Build automated causal discovery systems leveraging knowledge graphs, LLMs, and document understanding to uncover relationships between business actions and outcomes - Develop NLP and GenAI applications including event detection, sentiment analysis, cross-platform clustering, and multilingual insight synthesis - Partner with engineering teams to productionize models using AWS services (SageMaker, EMR, Lambda, Kinesis, Glue) with clear POC-to-production handoff criteria - Mentor junior scientists and contribute to building a research culture that balances high-risk, high-reward innovation with reliable product delivery - Author internal and externally-facing scientific publications and technical documents A day in the life 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: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 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 Amazon Community Operations (CommOps) works to anticipate our communities’ needs and build positive net impact anywhere we operate. From Operational Excellence to Community Outreach, Community Operations is defining what it means to be a positive presence in the community. This is a fast-paced, start-up environment where creative problem solving, operational excellence, and building relationships are the core of our day-to-day work.

Applied Scientists on the AWS Vulnerability Management (AVM) Builder Enablement team use program analysis, machine learning and generative AI for managing vulnerabilities and securing the software supply chain across AWS. Our scope covers the third-party open-source packages, language ecosystems, operating systems and kernels that AWS services are built on. Our goal is to take builders out of the remediation loop. When a vulnerability is disclosed or a package is compromised, our systems work out whether it actually affects AWS, generate the fix, check that it's safe and ship it at AWS scale, with one click or no clicks at all. New vulnerabilities are disclosed every day in the software AWS depends on, and finding and fixing them by hand doesn't scale. We build systems that detect vulnerabilities and work out whether the vulnerable code can actually be reached and exploited in a given service. They rank issues by real risk rather than raw severity scores, then fix them automatically. That can mean upgrading dependencies across ecosystems like Java, Python, JavaScript, Go and Rust, fixing breaking API changes, backporting patches, or patching operating systems and kernels across the fleet. Just as important, we check that each fix is correct and safe, so it can merge with little or no human review. Beyond known vulnerabilities, we protect the integrity of the software supply chain itself. Modern software is assembled more than it is written, and every dependency is a potential way in for attackers. Securing it means knowing what's in our software, where it came from and whether it can be trusted. Our work here includes building and analyzing software bills of materials (SBOMs), verifying package provenance and build integrity, and detecting malicious or compromised packages before they reach production. Our methods range from static and dynamic program analysis to LLM-based agents for code repair and upgrades. This is a chance to shape a fast-growing area that directly affects the security of AWS and its customers. You'll apply your technical depth in a fast-moving environment, and your work will reach thousands of AWS builders and services. Key job responsibilities • Design and build scientific approaches to vulnerability management. This includes vulnerability detection, reachability and exploitability analysis, dependency resolution, and prioritizing risk across third-party packages, operating systems and kernels. • Build program analysis techniques (static analysis, dataflow and call-graph analysis, binary and source analysis) that tell us precisely whether a vulnerability affects a given service or host. • Apply generative AI and agentic coding methods to fix vulnerabilities automatically. Examples include upgrading dependencies, fixing breaking API changes, backporting patches and migrating code across language versions. • Develop ways to check that automated fixes are correct and safe, such as test generation, behavior-equivalence checking, build and runtime checks, and confidence scoring, so changes can merge with little or no human review. • Own the design, experiments and delivery of well-defined science problems from start to finish. Turn ambiguous problems into measurable experiments and deliver solutions with measurable impact, such as faster time to fix, higher fix success rates and fewer false positives. • Work with engineers to turn research prototypes into production systems that run reliably across AWS's codebases and fleet. • Work with security, OS and kernel, and builder-tools partner teams to understand their remediation challenges and data. Find opportunities where science can make a difference. • Stay current with research in software security, program analysis, and ML for code. Contribute to internal and external publications where appropriate. • Share your knowledge through design reviews, documentation and mentoring of junior scientists and engineers. About the team The AWS Vulnerability Management (AVM) Builder Enablement team protects AWS from security risks in the software it depends on. Our mission is to take builders out of the remediation loop. We find the issues that matter, fix them automatically and ship the fixes safely at AWS scale. Scientists and engineers on the team work side by side, turning research in program analysis and generative AI into production systems that reach thousands of AWS builders. 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.

Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to join their team. CXBT's mission is to create best-in-class AI agents that seamlessly integrate multimodal inputs, enabling natural, empathetic, and adaptive interactions. We leverage advanced architectures, cross-modal learning, interpretability, and responsible AI techniques to provide coherent, context-aware responses augmented by real-time knowledge retrieval. As part of CXBT, we have a vision to revolutionize how we understand, test, and optimize customer experiences at scale. Where traditional testing approaches fall short, we create AI-powered solutions that enable rapid experimentation, de-risk product launches, and generate actionable insights, all before a single real customer is impacted. Be a part of our agentic initiative and shape how Amazon leverages artificial intelligence to run tests at scale and improve customer experience. As an applied scientist, you will research state-of-the-art techniques in agent-based modeling, and lead scientific innovation building foundational agentic simulation capabilities. If you are passionate about the intersection of AI and human behavior modeling, and want to fundamentally influence how Amazon tests and improves customer experiences, this role offers a great opportunity to make your mark. Key job responsibilities - Design and implement frameworks for multi-modal content handling and creating representative, diverse agents that faithfully capture real-world characteristics - Use state-of-the-art techniques in user modeling and behavioral simulation to build robust agentic frameworks - Research and implement novel algorithms and modeling techniques. - Acquire and curate diverse datasets while ensuring user privacy. - Create robust evaluation metrics and test sets to assess language model performance. - Innovate in data representation and model training techniques. - Apply responsible AI practices throughout the development process. - Write clear, scientific documentation describing methodologies, solutions, and design choices A day in the life Our team is dedicated to improving Amazon's products and services through evaluation of the end-to-end customer experience using both internal and external processes and technology. Our mission is to deeply understand our customers' experiences, challenge the status quo, and provide insights that drive innovation to improve that experience. Through our analysis and insights, we inform business decisions that directly impact customer experience as customers of new GenAI and LLM technologies. About the team CXBT is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers).

Have you ever wanted to solve a mystery or be part of solving a case? Are you fascinated by detective stories or crime shows on TV? Do you love to manage huge volumes of data, have a science background and enjoy solving complex problems? WWOS Tech is transforming into an AI-first security technology organization, and we're seeking an exceptional Applied Science Manager to anchor this transformation. As the Science & BI Lead, you'll own the enterprise AI/ML roadmap, lead an organization of scientists and BIEs, and deliver production AI and ML models that will generate 1M+ efficiency hours annually. Key job responsibilities Lead AI/ML Strategy & Delivery - Own the enterprise AI/ML roadmap across UNITE (theft detection, investigation automation), PRISM (operational disruption, risk management) and other WWOS Tech products - Deliver net-new production AI models by EOY 2026 aligned to WWOS SPS goals: reduce theft/fraud loss from 0.30% to 0.19% of GMS and transform incident preparedness from reactive to proactive. - Establish AI/ML delivery standards: model quality gates, bias detection, responsible AI compliance (Amazon Trust principles, EU AI Act), and production readiness criteria - Build centralized model registry, shared experimentation platform (SageMaker), and MLOps infrastructure in partnership with Data Engineering Build & Scale High-Performing Teams - Lead Science & BI pillar within WWOS Tech: grow Science team over next 18 months, manage 3 BIE managers overseeing BIEs across EESN, Ops Disruption, and Business Reporting teams - Recruit, onboard, and retain top AI/ML talent in a highly competitive market; develop career paths for Scientists, ML Solutions Architects, and BIEs transitioning to AI-enabled strategic advisors - Drive AI literacy across all of WWOS organization: 100% AI-trained by EOY 2026 across Technical, Leadership, and Cross-Skill tracks - Establish operating rhythm: weekly Science pillar sync, bi-weekly cross-pillar integration reviews, monthly AI portfolio health inspections Partner with Business & Technical Leaders - Translate ambiguous business problems into AI/ML solutions through direct partnership with field leaders, program vertical leaders, and WWOS senior leadership. - Represent WWOS Tech in Amazon-wide AI/ML forums: AWS AI partnerships, responsible AI governance, GOS AI forums Drive Responsible AI & Governance - Ensure all AI models touching sensitive security data meet Amazon's responsible AI bar and evolving regulatory requirements (GDPR, EU AI Act) - Implement bias detection, model explainability and human-in-the-loop mechanisms for high-risk applications - Conduct quarterly AI risk assessments with Legal, InfoSec, and Privacy teams; maintain AI model inventory and compliance dashboard - Partner with AI Ethics & Governance Specialist to establish enterprise-wide responsible AI frameworks About the team We are a team that cares about your work-life balance, while challenging you to solve problems at high scale. You will be part of a strong team in a fast-paced, start-up environment where agile development is embraced and innovation is encouraged. You will get support and resources from some of the smartest people in the industry to continue your personal and professional growth. You'll be joining a fun team that prides itself on a great work environment with an inclusive group of people that loves working together towards a common goal and make history in launching a new strategic service in the industry.

The Amazon Connect Interactive AI and Engagement organization was formed in April 2025 to bring together Contact Lens, Q in Connect, and Flows/Lex into one organization, responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. We are reimagining customer engagement to enable companies to deliver proactive and personalized experiences (in websites, mobile apps, and traditional contact center channels including voice, messaging, and email) that discern and resolve end-customers' intent before problems ever arise. To succeed, we need a unified science strategy and approach to power 'AI-throughout' customer experiences that leverage humans in the loop when required to meet business goals. We seek to hire a Director of Applied Science who will define and execute that strategy, and organization required. This leader will push the technical boundaries in generative AI science, shaping the industry, while influencing and leading key product investments across Connect service teams and leadership. The business opportunity is substantial. We are executing to be the leader irrespective of the ultimate balance between proactive end-customer self-service and agent-assisted workloads. To do so, science innovation will be pivotal to help achieve our ambitious goals, differentiating Amazon Connect from our competitors. Key job responsibilities The Director of Applied Science will lead us to propose and deliver an organizational plan that is aligned to a unified science strategy. This science strategy will enable data-driven, AI-powered continuous optimization across self-service, agent assistance, and manager assistance workloads. This leader will inherit an existing applied science team, and possibly (strategy dependent) a data integrations team engineers. We believe that the strategy this leader defines will require additional applied scientists. Beyond direct team ownership, this leader will be responsible for cross-AWS science collaboration with Bedrock, AGI, Q, and Transcribe organizations.

Alexa+ is the world’s best Generative AI powered personal assistant / agent for consumers, and is becoming the conversational AI interface for Amazon services with the launch of Alexa for Shopping on Amazon.com and Amazon mobile app. At Alexa Ads, we are creating industry's first and most advanced Agentic Advertising products to drive Agentic Commerce. We are seeking an Applied Scientist to join our newly expanding team in India focused on Alexa Agentic/Conversational Ads and Personalization. In this role, you will build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions. You will work closely with other scientists, engineers, and product managers to take models from conception to production. Key job responsibilities - Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization. - Conduct hands-on data analysis and build scalable ML pipelines. - Design and run A/B experiments to measure the impact of new models on customer experience and ad performance. - Collaborate with software development engineers to deploy models into high-scale, real-time production environments. About the team We are building a new science team in Bangalore to solve some of the most impactful problems in computational advertising. This isn't about tweaking existing models as we are rethinking how ads are ranked, priced, and personalized across voice-first and screen-first surfaces. These are problems that don't have textbook solutions. Key points to note about the team: 🧪 Greenfield team - you are not joining a mature org with rigid processes. You will shape the science roadmap, pick the problems, and define the culture from day one. 📈 Direct business impact — your models directly drive revenue. No yearly cycles to see if your work matters. 🌏 Global scope, local autonomy — collaborate with scientists and engineers across Seattle, Sunnyvale, and Bangalore, but own your problem space end-to-end. 🎓 Ship AND Publish: We encourage top-tier publications (NeurIPS, ACL, EMNLP, KDD, ICML, WWW) while ensuring your research hits production.

RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software. Our fleet of delivery robots operates globally today, generating vast amounts of robotic real-world data. By utilizing state-of-the-art Vision-Language-Action (VLA) models, large-scale generalist models (like Transformers), generative AI, and similar methods, we can leverage this pool of data to significantly enhance its autonomy, navigation, and manipulation skills. In this role, you will develop multi-modal models that enable robots to autonomously generate actions from demonstrations, real-time sensor data, and natural language commands. We are seeking an expert in VLA models, imitation learning, and generative AI techniques with a deep knowledge of supervised, and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI we invite you to join us in shaping the future of intelligent robotics. Key job responsibilities Develop and implement Vision-Language-Action (VLA) models, generalist robot transformers, and imitation learning algorithms (e.g., diffusion policies) to enable robots to autonomously execute complex tasks. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and navigation tasks, with a focus on spatial reasoning and generalization. Streamline the data collection and training workflow to efficiently expand model capabilities with new tasks and data sources. Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data. Optimize and distill networks for real-time deployment on the edge (e.g. Nvidia Jetson Thor). Build, lead and mentor an exceptional team of software engineers. Provide expert guidance to product managers and executives for strategic decision-making. Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.