Careers

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

As a Senior Applied Scientist specializing in lead scoring and deep learning modeling, you will tackle complex challenges in machine learning and deep learning to redefine how our business engages with customers. You will design and deploy high-impact models that drive customer segmentation, adaptive recommendations, and predictive lead and account prioritization. Leveraging your expertise in deep learning, representation learning, and general modeling, you'll help build solutions that directly influence business outcomes, collaborating with cross-functional teams to turn novel research into scalable, production-grade systems. Key job responsibilities * Design and deploy predictive lead scoring models to optimize customer acquisition, conversion, and retention strategies using advanced techniques like survival analysis, graph networks, or transformer-based architectures. * Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference. * Publish research, file patents, and stay ahead of industry trends in the marketing science, propensity modeling, and customer journey prediction domains. * Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance scoring accuracy across account and lead levels. * Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly. * Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker, or MLflow and other internal tools. * Participate in science reviews to raise the science bar in our organization. This includes reviewing your work and the work of others. * Mentor junior scientists on ML methodology, experimentation design, and production best practices. * Define offline and online evaluation frameworks; establish success metrics tied to business outcomes (conversion rates, pipeline generation). About the team The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.

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.

As an Applied Scientist II in the Alexa Conversational Modelling Intelligence team within Alexa AI, you will drive model post-training for Large Language Models that power Alexa+. You'll adopt and adapt state-of-the-art techniques — including supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation — running rigorous experiments and translating findings into production-ready solutions that directly improve the customer experience for millions of users worldwide. You will own the full model development cycle from data curation through training, evaluation, and deployment. Your day-to-day will involve developing evaluation methods and metrics, diagnosing model defects, optimizing model training pipelines, and iterating on recipes to move concrete quality and efficiency benchmarks. You'll write clean, reproducible code, contribute to shared tooling, and collaborate closely with scientists and engineers to bring models from experimentation to scale. You are technically curious, experiment-driven, and motivated by real customer impact. You are an expert in LLM post-training. You will also advance the state of the art by publishing at top-tier NLP/ML conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) — contributing to the broader research community while grounding your work in measurable outcomes. Key job responsibilities * Own the full model development cycle — from data curation through training, evaluation, and deployment. * Develop and apply post-training techniques: supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation. * Build evaluation methods and metrics, and diagnose model defects to target the highest-impact improvements. * Optimize model training pipelines and iterate on recipes to move concrete quality and efficiency benchmarks. * Write high-quality documentation on methods and experiment outcomes, and communicate findings clearly to stakeholders. A day in the life Post-training is one of the most active frontiers in LLMs right now. The field has moved from scaling pretraining to getting more out of models afterward through RL, reasoning recipes, and preference optimization. You'll work on these techniques directly, on a product used by millions of customers every day. A typical day: review overnight training runs and dashboards, dig into model defects to form hypotheses, then curate data and iterate on a recipe, improving shared tooling along the way. You'll sync with scientists and engineers to unblock the path to production, and write up your findings for stakeholders. It's fast-moving — a good idea can reach millions of customers within weeks. About the team The Alexa Conversational Modelling Intelligence team builds industry-leading LLM-based conversational technologies that customers love. Our mission is to push the envelope in LLMs for Alexa to deliver the best-possible customer experience. As an Applied Scientist, you'll contribute directly to that mission through model development and experimentation.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Manager III, Economist Job Location: Sunnyvale, California Job Number: AMZ9803624 Position Responsibilities: Independently manage a team of economists and/or scientists in developing strategic economic analyses and demand estimation models. Translate business questions into econometric methodologies and causal inference analyses. Communicate economic insights to non-technical audiences to guide strategic-level, high-impact business decisions. Scale economic models through cross-functional collaboration with engineering teams. Establish scientific quality standards and research priorities. Drive operational efficiency and research excellence across the team. 40 hours / week, 8:00am-5:00pm, Salary Range: $201,300/year to $272,400/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI at the same time. This role is a chance to define and lead the science strategy for the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Senior Data Scientist who operates at the intersection of applied ML, agentic AI, and security; and who can set technical direction across ambiguous, undefined problem spaces. You won't just build models; you'll decide which problems are worth solving, architect the scientific approach for an entire product area, and raise the bar for how the team applies science. You'll partner with senior and principal engineers, applied scientists, security researchers, and PMs, and your judgment will shape roadmaps, not just deliverables. This is a role for someone who thrives in ambiguity, influences without authority, and turns "too ambitious" into shipped reality. Key job responsibilities - Set the science direction for a product area: Define the modeling strategy, scientific approach, and success metrics for entire categories of AI-first security capabilities, agentic systems, anomaly detection, threat classification, and automated response across multi-cloud environments. Decide where science can move the needle and where it can't. - Own the hardest, most ambiguous problems: Take on undefined, open-ended challenges where the path isn't clear, the data is messy or scarce, and the stakes are high. Frame the problem, choose the approach, and bring others along. - Build with AI to build AI and define how the team does it: Drive adoption of agentic coding tools, LLM-powered workflows, and experimental AI tooling across the science org. Establish the practices that multiply velocity for every scientist, not just yourself. - Architect agentic intelligence: Lead the design of models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready at scale. Own the science architecture decisions others build on. - Drive technical strategy across teams: Influence roadmaps, dive deep with senior and principal scientists and engineers, and align cross-functional partners around a shared scientific vision. Your recommendations shape what the team invests in next. - Prototype, validate, and scale: Turn ambiguous hypotheses into prototypes in days, validate with real customer signal, and chart the path from prototype to production system that runs reliably at Amazon scale. - Communicate to influence at the executive level: Translate complex modeling results and scientific trade-offs into clear recommendations for engineers, product leaders, and senior executives. Drive organizational decisions with data and earn trust across the company. - Raise the bar and grow others: Mentor data scientists and applied scientists, lead technical and science reviews, and champion AI-first development practices. Shape the science culture and hiring bar of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning setting direction in a roadmap review; making the call on which science investments will have the biggest customer impact and then dive into architecting an evaluation framework that the whole team will build on. Before lunch, you're pair-prompting with an agentic coding assistant to validate a new approach, then unblocking a teammate stuck on a thorny modeling problem. In the afternoon, you lead a design session with senior and principal scientists and engineers, then distill it into a crisp recommendation for senior leadership. You own ambiguous problems end to end, define how the team works, and see your decisions ripple across the product. This is where builders who want to lead with science come to do their best work. About the team Why AWS? 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. Inclusive Team Culture 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 (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & 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, mentorship 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

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 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.

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!