Careers

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.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Demand Tech Experience (DTx), as an org, owns the Amazon Ads Console — the advertiser-facing workspace for creating and managing campaigns, reviewing performance, and acting on recommendations used by millions of advertisers. Our team, DTx Science, is the centralized science team within DTx, dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. In this role, you will work closely with business leaders, stakeholders, and cross-functional teams to drive program success through ML- and agentic-driven solutions. You will shape the applied science roadmap, promote a culture of data-driven decision-making, and deliver significant business impact for millions of advertisers worldwide and the company using advanced data techniques and applied science methodologies. Key job responsibilities As an Sr. Applied Scientist on this team, you will - Lead the development of agent evaluation, agent impact measurement, agent experimentation, and agent advertiser experience - Lead high-ambiguity projects and drive alignment across teams on science and engineering solutions. - Drive adoption of state-of-the-art scientific technologies in generative AI, reinforcement learning, causal inference, classical machine learning, and natural language processing to improve the team's existing science solutions. - Translate complex scientific challenges into clear and impactful solutions for business stakeholders. - Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. - Foster collaborations between scientists to move faster, with broader impact. - Regularly engage with the broader scientific community with presentations, publications, and patents. About the team The DTx Science team is the centralized science team within Demand Tech Experience (DTx), dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. Improve advertiser experience: Ad Console Personalization and Recommendation Development aim to improve the advertiser ad console experience by providing customized and personalized console experiences for each user and increasing their ad performance with relevant, science-backed onboarding and oppertunty recommendations. Protect advertiser experience: Ad Console Experimentation and DVA Agent Evaluation and Impact Measurement protect the advertiser experience by supporting the organization in making data-driven decisions to launch UX and agentic features that benefit advertisers.

Do you want to translate cutting-edge science into products that expand financial access for millions? At International and Emerging Stores Payments (IESP), we build payment solutions for communities and geographies that Amazon's selection and prices do not yet reach because of payment barriers. These markets make up roughly 80% of the world's countries and 85% of its population. As an Applied Scientist II on our team, you will design, build, and ship machine learning and generative-AI solutions that remove those barriers and advance financial inclusion, working at the intersection of research and production alongside scientists, engineers, and economists. If you are a curious, self-motivated researcher who turns ambiguity into clarity, we would like to hear from you. What you will work on: • Build models that recommend the most suitable payment method for each customer—for example installments, cards, or local payment options—and identify the right customers to reach, balancing customer preference, cost, and risk. • Take models to production behind a real-time, low-latency personalization layer that serves live shopping traffic at scale. • Build multi-agent generative-AI systems that turn unstructured inputs—customer feedback, documents, and operational data—into reliable, decision-ready insight, with verification that guards against model hallucination. • Help customers discover and adopt installments and alternative payment methods. • Design and analyze large-scale online experiments and causal measurement to validate real customer and business impact. Key job responsibilities • Design, develop, and evaluate machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems. • Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise. • Write clean, maintainable, and efficient code that integrates smoothly into larger systems and meets a high bar for operational excellence. • Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems. • Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code. • Publish at peer-reviewed venues and file patent applications where appropriate. A day in the life You will start your day reviewing experiment results and refining model architectures based on new insights. Throughout the day you will alternate between writing research code, analyzing data, and discussing technical approaches with teammates, working closely with engineering to build your ideas and take them to production. You will regularly present findings and propose new ideas, and you may write research papers and submit patent applications. About the team We are the science team for International and Emerging Stores Payments. We build solutions that directly shape how customers pay and that drive conversion across Amazon's fast-growing international markets—and there is no shortage of hard science and engineering problems, since these markets span most of the world's countries and the majority of its customers. We value scientific rigor, creative problem-solving, and a genuine commitment to learning and growth. If you are excited about building impactful solutions and advancing financial inclusion through applied science, we would love to have you join us.

Do you want to translate innovative science into products that expand financial access for millions? At International and Emerging Stores Payments (IESP), we build payment solutions for communities and geographies that Amazon's selection and prices do not yet reach because of payment barriers. These markets make up roughly 80% of the world's countries and 85% of its population. As an Applied Scientist II on our team, you will design, build, and ship machine learning and generative-AI solutions that remove those barriers and advance financial inclusion, working at the intersection of research and production alongside scientists, engineers, and economists. If you are a curious, self-motivated researcher who turns ambiguity into clarity, we would like to hear from you. What you will work on: • Build models that recommend the most suitable payment method for each customer—for example installments, cards, or local payment options—and identify the right customers to reach, balancing customer preference, cost, and risk. • Take models to production behind a real-time, low-latency personalization layer that serves live shopping traffic at scale. • Build multi-agent generative-AI systems that turn unstructured inputs—customer feedback, documents, and operational data—into reliable, decision-ready insight, with verification that guards against model hallucination. • Help customers discover and adopt installments and alternative payment methods. • Design and analyze large-scale online experiments and causal measurement to validate real customer and business impact. Key job responsibilities • Design, develop, and evaluate machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems. • Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise. • Write clean, maintainable, and efficient code that integrates smoothly into larger systems and meets a high bar for operational excellence. • Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems. • Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code. • Publish at peer-reviewed venues and file patent applications where appropriate. A day in the life You will start your day reviewing experiment results and refining model architectures based on new insights. Throughout the day you will alternate between writing research code, analyzing data, and discussing technical approaches with teammates, working closely with engineering to build your ideas and take them to production. You will regularly present findings and propose new ideas, and you may write research papers and submit patent applications. About the team We are the science team for International and Emerging Stores Payments. We build solutions that directly shape how customers pay and that drive conversion across Amazon's fast-growing international markets—and there is no shortage of hard science and engineering problems, since these markets span most of the world's countries and the majority of its customers. We value scientific rigor, creative problem-solving, and a genuine commitment to learning and growth. If you are excited about building impactful solutions and advancing financial inclusion through applied science, we would love to have you join us.

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3GPP-compliant service to unmodified commercial handsets. The Role: Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Research, design and specify RRM algorithms for Amazon Leo’s 3GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services. • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO). • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum. • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity. • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation. • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior. • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures. • Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment. • Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. Come work at Amazon! The Role: Be part of the team defining the overall communication system and architecture of Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in integration, verification and testing of the systems especially focused on MAC and higher layer testing. This role will also be responsible developing and testing advanced L1/L2/L3 concept to improve the performance and reliability of the LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. In this role you will: - Work within a project team and take the responsibility for the Leo’s communication system design, system integration and verification. - Work as a part of the team in building a suite of system and network simulation services in Matlab / C++ / Python - Develop requirements from system level to HW/SW level and define test cases associated with the requirements. - Identify additional HW and SW that are needed for the purposes of verification and guide the HW/SW development team in the development of these test solutions/tools// - Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements - Write scripts / code for functions / features required for specific simulation, testing and verification of given RF system EXPORT CONTROL REQUIREMENTS Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Amazon Economics is seeking Structural IO Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available STRUC internships in 2027. Key job responsibilities As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: - Analyze large-scale datasets using structural econometric techniques to solve complex business challenges - Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions - Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization - Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value - Build and refine comprehensive datasets for in-depth structural economic analysis - Present complex analytical findings to business leaders and stakeholders

AWS Startups supports hundreds of thousands of founders globally — from first credit to scaled production workload. Data and machine learning are how we identify high-potential startups early, personalize the guidance we deliver, and decide where to invest next. We recently launched AWS Startup Advisor, an AI-powered service that brings AWS expertise directly into the developer tools founders already use, and we need a scientist to build the ML capabilities that power its proactive recommendations. In this role, you will own science problems end-to-end — from the data foundation that unifies signals about founders and their products, through model design and evaluation, to production deployment serving hundreds of thousands of startups. If you want your models to reach real founders the same week you ship them, this is the role. Key job responsibilities - Own the science for problem areas end-to-end: frame the problem, define the data strategy, build and evaluate ML models for recommendation systems, startup segmentation, and fraud detection, and deploy them into production. - Apply generative AI and large language models to personalize the technical guidance founders receive, including retrieval, ranking, and evaluation of LLM-powered experiences. - Design and run experiments that keep model quality quantified and defensible, making clear trade-offs between accuracy, latency, and cost as you balance rapid iteration with production reliability. - Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate results clearly to both technical and non-technical leaders. - Raise the scientific bar through design and code reviews, mentor other scientists and engineers, and contribute to the broader scientific community through publications or peer reviews. A day in the life You might start the morning digging into a messy dataset to uncover a new segmentation signal, then shift to reviewing an A/B test that measures how a recommendation model is changing founder engagement. After lunch you could pair with an engineer to optimize inference latency for a fraud-detection model, then join a product review where you present trade-offs between two ranking approaches for AWS Startup Advisor. Expect to regularly move between hands-on modeling work and cross-team conversations that shape what gets built next. About the team The AWS Startups team builds products and platforms that support startup customers at every stage of their journey — from onboarding and credit programs to AI-powered guidance and scale solutions. We partner with business development, field marketing, and solutions architecture teams worldwide, and our portfolio serves hundreds of thousands of startups globally. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder, and you will help shape the scientific direction that gets us there.