Careers

Amazon's Identity Security & Abuse Prevention (ISAP) team is seeking an Applied Scientist to join our team. We discover, analyze, and quantify security risks across Amazon's identity and authentication landscape, transforming complex behavioral patterns into actionable intelligence that empowers teams to proactively defend against abuse and unauthorized access. In this role, you will design, build, and own machine learning systems that detect abuse patterns, classify threats, and automate enforcement across sensitive datasets spanning multiple Amazon verticals. You will independently frame ambiguous detection problems, develop novel approaches to abuse prevention, and deploy production ML systems that directly protect Amazon customers and sellers at scale. You will work at the intersection of applied science and security operations, translating complex abuse vectors into scalable detection capabilities. This is a high-ownership role where your models and systems run autonomously in production, making real-time decisions that prevent fraud and abuse. You will own both existing detection capabilities (improving precision, recall, and coverage of current models) and greenfield science (designing and deploying new detection systems for emerging threat vectors). You will lead experimental design, extend or invent methodologies for your domain, mentor junior scientists, and contribute to the team's scientific roadmap. You will partner with investigators, security engineers, and data engineers to build end-to-end detection and enforcement pipelines, and you will leverage GenAI, LLMs, and AI-agent architectures to advance our abuse prevention capabilities. Key job responsibilities - Design, develop, and deploy production ML systems for abuse pattern detection, anomaly detection, threat classification, and automated enforcement across multiple Amazon verticals - Independently frame ambiguous security and abuse problems into well-defined scientific questions, propose detection approaches, and drive them from hypothesis through production deployment - Own and improve existing detection models end-to-end: monitor for drift, diagnose degradation, retrain, and extend coverage as abuse patterns evolve - Build and maintain graph-based entity analysis, identity resolution, and modus operandi classification systems that link bad actors across accounts, devices, and behavioral signals - Design and execute rigorous experiments (A/B testing, offline evaluation, statistical validation) to measure model performance and quantify business impact - Architect and deploy GenAI and LLM-based solutions for investigation automation, case classification, and intelligent knowledge retrieval - Contribute to the team's scientific roadmap by identifying high-value detection opportunities, proposing new approaches, and driving prioritization of science investments - Publish research findings in internal Amazon papers and at external peer-reviewed conferences; contribute to the broader scientific community - Partner with investigators, security engineers, and data engineers to understand abuse patterns, translate operational insights into model features, and ensure detection systems drive real enforcement actions A day in the life Your morning might start with reviewing model performance dashboards for a classifier you deployed last month, noticing a subtle precision drop that suggests adversarial adaptation. You diagnose the drift, propose a feature addition to counter the new pattern, and kick off a retraining job. Mid-morning, you lead a design review on a new graph-based detection approach you developed to identify organized abuse rings operating across multiple verticals. After lunch, an investigator shares a newly identified modus operandi, and you explore the data to determine if the pattern is learnable at scale, sketching an experimental design. Late afternoon, you pair with a junior scientist on their anomaly detection model, helping them refine their evaluation methodology and avoid a common statistical pitfall. You close the day by drafting a section of a research paper on your entity resolution approach, preparing it for internal peer review. About the team The ISAP SafeGuard team mixes long-term, high-impact projects with near-term innovative solutions to prevent abuse across Amazon. We balance Bias for Action, Dive Deep, Invent and Simplify, and Customer Trust daily. Our team embraces new approaches, technology, and innovation while ensuring our solutions are scalable, accurate, and drive action. We work with some of the most sensitive data at Amazon, which requires thoughtful engineering, strict access controls, and a strong sense of responsibility. If you are energized by building ML systems that directly protect customers and sellers from bad actors, at scale, this is the team for you. 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.

Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure. We are looking for a Applied Scientist to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Software Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities - Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams - Priority management - manage diverse requests and dependencies from teams - Process improvements – define, implement and continuously improve delivery and operational efficiency - Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations - Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better. Internal job description

The Region Flexibility Engineering (RFE) team builds and leverages foundational infrastructure capabilities, tools, and datasets needed to support the rapid global expansion of Amazon's SOA infrastructure. Our team focuses on robust and scalable architecture patterns and engineering best practices, driving adoption of ever-evolving and AWS technologies. RFE is looking for a passionate, results-oriented, inventive Data Scientist to refine and execute experiments towards our grand vision, influence and implement technical solutions for regional placement automation, cross-region libraries, and tooling useful for teams across Amazon. As a Data Scientist in Region Flexibility, you will work to enable Amazon businesses to leverage new AWS regions and improve the efficiency and scale of our business. Our project spans across all of Amazon Stores, Digital and Others (SDO) Businesses and we work closely with AWS teams to advise them on SDO requirements. As innovators who embrace new technology, you will be empowered to choose the right highly scalable and available technology to solve complex problems and will directly influence product design. The end-state architecture will enable services to break region coupling while retaining the ability to keep critical business functions within a region. This architecture will improve customer latency through local affinity to compute resources and reduce the blast radius in case of region failures. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency. Key job responsibilities As an RFE Data Scientist, you will work closely with product and technical leaders throughout Amazon and will be responsible for influencing technical decisions and building data-driven automation capabilities in areas of development/modeling that you identify as critical future region flexibility offerings. You will identify both enablers and blockers of adoption for region flex, and build models to raise the bar in terms of understanding questions related to data set and service relationships and predict the impact of region changes and provide offerings to mitigate that impact. About the team The Regional Flexibility Engineering (RFE) organization supports the rapid global expansion of Amazon's infrastructure. Our projects support Amazon businesses like Stores, Alexa, Kindle, and Prime Video. We drive adoption of ever-evolving and AWS and non-AWS technologies, and work closely with AWS teams to improve AWS public offerings. Our organization focuses on robust and scalable solutions, simple to use, and delivered with engineering best practices. We leverage and build foundational infrastructure capabilities, tools, and datasets that enable Amazon teams to delight our customers. With millions of people using Amazon’s products every day, we appreciate the importance of making our solutions “just work”.

The Shopping Convo Foundations Team - Pre-purchases Science is looking for an Applied Scientist with expertise in Artificial Intelligence and Machine Learning to drive scientific innovation that expands Amazon's product catalogue. Our goal is to leverage AI/ML solutions to enhance catalogue coverage with high precision. In this role, you will research and develop novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You will design and develop state-of-the-art ML models, conduct rigorous experimentation, and translate scientific breakthroughs into production-ready solutions. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale.

The Amazon Cryptographic Libraries (ACL) team builds the cryptography that AWS services and a growing open-source community depend on, including AWS-LC, our FIPS-validated open-source libcrypto. As an Applied Scientist on the team, your primary focus will be formal verification: building machine-checked proofs that cryptographic implementations are correct. You will also contribute to algorithm implementation, assembly level optimization, and the adoption of post-quantum cryptography. You will work alongside senior scientists on the team, building deep expertise in an environment where your proofs and code ship to effectively every AWS service. This is a role where an early-career scientist gets both rigorous mentorship and immediate production-scale impact. Key job responsibilities - Develop and maintain machine-checked proofs of correctness for cryptographic implementations in AWS-LC, working alongside senior scientists. This is the core of the role. - Specify the functional behavior of low-level cryptographic code (C, assembly) in formal notation and verify it using interactive theorem provers (HOL Light, Isabelle/HOL, or similar). - Apply formal methods, program analysis, and rigorous testing to raise the assurance bar of a security-critical, widely deployed codebase. - Contribute to the implementation and optimization of cryptographic algorithms, including post-quantum constructions (ML-KEM, ML-DSA, SLH-DSA), for production use. - Collaborate with SDEs, security engineers, and partner teams to translate verified implementations into production-grade, FIPS-validated software. - Grow your expertise through publications, open-source contributions, and engagement with the broader formal-methods and cryptographic research communities. A day in the life You take a cryptographic primitive that needs to be provably correct. Working with a senior scientist or an ARG partner, you write the formal specification of its behavior, develop the proof in an interactive theorem prover, and iterate until the machine checks it end to end. Some days you are debugging a proof obligation that does not discharge; other days you are reading a paper on a new verification technique or helping refine an algorithm implementation so it is both fast and amenable to proof. Your proofs back code that is validated for FIPS and deployed across AWS, so you operate at an assurance bar most scientists never encounter this early in their career. About the team ACL owns AWS-LC (Amazon's FIPS-validated libcrypto), the Amazon Corretto Crypto Provider (ACCP), and managed third-party cryptographic libraries, the cryptographic foundation under nearly every AWS service and a growing set of external open-source projects. Applied Scientists on the team own algorithm-level and assembly performance work and partner deeply with Amazon's Automated Reasoning Group on formal verification. The team has senior scientists at L6 and L7 who actively mentor and collaborate, so an early-career scientist gets both research depth and production-scale reach from day one.

Amazon Alexa for Shopping (Rufus) Experience Science team is seeking a highly motivated Scientist who is passionate about building next-generation agentic shopping experiences. In this role, you will be developing Agentic Language Models to unlock new shopping experiences. Prior experiences with LLM Post-training techniques, Reinforcement Learning Training and business specific RL gym creation is preferred. You will collaborate closely with a multidisciplinary team of scientists, engineers, product managers, and designers to deliver these experiences across multiple Rufus customer-facing features. You will thrive in this role if you enjoy bringing latest LLM post-training research into everyday life—both for customers and for yourself. There’s nothing quite like realizing that a model you deployed yesterday is already improving your own shopping experience today. You will work side by side with scientists and engineers in a fast-paced environment, driving rapid model development and experimentation. You’ll also have access to Amazon’s rich datasets, AWS’s massive computational resources, and a network of world-class science and engineering leaders across the company. Key job responsibilities Execute the science vision and roadmap.
Develop data-driven solutions for the real-world, large scale problems in alexa for shopping.
Deliver and maintain software and models in the production environment.
Collaborate cross-functionally between product, design, and engineering.

We are searching for a talented candidate with expertise in orbital mechanics and spaceflight navigation, including LEO Satellite Orbit Determination. This position requires experience in simulation and analysis of spacecraft orbital mechanics and sequential orbit determination methods, including Extended Kalman Filters (EKF) and/or Unscented Kalman Filter (UKF). Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Key job responsibilities - Perform spacecraft maneuver or navigation analysis in support of multi-disciplinary trades within the Amazon Leo team. - Contribute to prototype software development of flight algorithms. - Test and assess navigation software for integration into flight systems. - Assess and trouble-shoot the performance of Leo on-board GNSS hardware and software systems. - Work closely with GNC engineers to manage on-orbit performance and develop flight dynamics operations processes. 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. A day in the life - Interacting with GNC teams to evaluate and troubleshoot satellite issues. - Working within the Flight Dynamics Research team to prioritize tasks. - Performing analysis, simulation, testing and documentation to address assigned tasks.

The Trust CX Innovations team is looking for an Applied Scientist with strong background in Generative AI space to build solutions that help in upholding customer trust for Alexa+. As an Applied Scientist in Trust CX innovations, you will be at the forefront of developing innovative solutions to critical challenges in AI trust and privacy. You'll lead research in trust-preserving machine learning techniques. We are working on revolutionizing the way Amazonians work and collaborate. You will help us achieve new heights of productivity through the power of advanced generative AI technologies. Key job responsibilities - Lead research initiatives in generative AI, focusing on LLMs, multimodal models, and frontier AI capabilities - Develop innovative approaches for model optimization, including prompt engineering, few-shot learning, and efficient fine-tuning - Pioneer new methods for AI safety, alignment, and responsible AI development - Design and execute sophisticated experiments to evaluate model performance and behavior - Lead the development of production-ready AI solutions that scale efficiently - Collaborate with product teams to translate research innovations into practical applications - Guide engineering teams in implementing AI models and systems at scale - Author technical papers for top-tier conferences - File patents for novel AI technologies and applications A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our trust-preserving experiences. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the team Who We Are: Trust CX Innovations is a strategic innovation team within Amazon Devices & Services that focuses on advancing AI technology while prioritizing customer trust and experience. Our team operates at the intersection of artificial intelligence, privacy engineering and customer-centric design. Our Mission: To pioneer trustworthy AI innovations that delight customers while setting new standards for privacy and responsible technology development. We aim to transform how Amazon builds AI products by creating solutions that balance innovation with customer trust.

Are you excited about leveraging state-of-the-art Computer Vision algorithms and large datasets to solve real-world problems? Join Amazon as an Applied Scientist Intern and be at the forefront of AI innovation! As an Applied Scientist Intern, you'll work in a fast-paced, cross-disciplinary team of pioneering researchers. You'll tackle complex problems, developing solutions that either build on existing academic and industrial research or stem from your own innovative thinking. Your work may even find its way into customer-facing products, making a real-world impact. Please note: This internship is a duration of 6 months full time with a start date in Jan-March 2027. The successful intern is required to be based in Melbourne and relocation allowance will be provided if you are based outside of Melbourne. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Computer Vision and Machine Learning - Contribute to research that could significantly impact Amazon's operations - Collaborate with a diverse team of experts in a fast-paced environment - Collaborate with scientists on writing and submitting papers to Tier-1 conferences (e.g., CVPR, ICCV, NeurIPS, ICML) - Present your research findings to both technical and non-technical audiences Key Opportunities - Collaborate with leading machine learning researchers - Access Amazon tools and hardware (large GPU clusters) - Address challenges at an unparalleled scale - Become a disruptor, innovator, and problem solver in the field of computer vision - Potentially deliver solutions to production in customer-facing applications - Opportunities to become an FTE after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!

Applied Scientists in AWS Science of Security are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty. Key job responsibilities The successful candidate will: * Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. * Own the design, implementation, and delivery for solutions that have a long-term quantifiable impact. *Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. * Develop fundamentally new solutions for business problems. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. 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.