Careers

AWS Support is looking for a high caliber Applied Scientist to build AI/GenAI experiences and foundations for Kumo Intelligent Tooling — the organization that owns products and services used by 10,000+ AWS Support staff to help 40,000+ unique monthly AWS customers, and is responsible for AWS Support’s strategic initiative to transform its business and delivery model through GenAI. We are looking for a Principal Scientist to join us and spearhead the AI revolution through intelligent solutions that assist customers and Support staff to troubleshoot and resolve technical issues. You are a hands-on contributor and will apply your knowledge to propose solutions, create software prototypes, and productize prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet ever-growing customer needs use cases. You have strong verbal and written communication skills, are self-driven and deliver high quality results in a fast-paced environment. You will play a pivotal role in shaping the definition, vision, design, roadmap and development of solutions from beginning to end for hard, previously unsolved problems. Key job responsibilities - Lead and conduct advanced research in Large Language Models (LLMs), GenAI, and Deep Learning, with a focus on developing novel algorithms, architectures, and methodologies for technical support. - Stay up-to-date with the latest advancements in AI, LLMs, and GenAI, and identify opportunities to leverage cutting-edge technologies to deliver new Support offerings and capabilities to AWS customers. - Lead by example, demonstrating technical excellence that other scientists aspire to follow, fostering a culture of innovation and knowledge sharing, and taking time to mentor and develop team members - Collaborate with cross-functional teams, including scientists, engineers, and product managers, to translate research findings into practical applications. - Partner across AWS AI/GenAI service teams to influence and drive investment prioritization and product direction. - Evangelize our AI/GenAI innovations, results, and impact to customers, partners, and AWS senior leaders. About the team Kumo is the global product and engineering organization for AWS Support, a multi-billion $ business. Our mission is to empower innovators to get the most out of cloud services. We build technology that reimagines how people and automation combine to solve problems, remove risks, build with excellence, and drive business impact. We own critical cloud services used by all AWS customers to build, optimize, and operate at scale, including AWS Health, Trusted Advisor, Well-Architected, re:Post, Support Center, and AWS Managed Services. We also own services that enable AWS support teams to provide mission-critical, customer-obsessed support to our customers, including Command Center (the console platform for 14,000 frontline staff and technical account managers), Kumo Case Management (the contact center platform for technical support), Tool Contribution (the platform for support staff to build and reuse troubleshooting tools), and Business Case Authorization (the service for controlling access to customer metadata based on business justifications). AWS Kumo is a dynamic, agile, and collaborative team of individuals with diverse backgrounds, located around the globe with larger teams in the U.S., Canada, and South Africa.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches

The Seller External Relations (SER) team ensures that Amazon is viewed globally by external audiences as a valuable, accessible, partner who respects and celebrates the success of its sellers. We develop strategic, data-supported, messaging and publish external reports that demonstrate and celebrate the positive experiences of sellers on Amazon to other sellers, the general public, and influentials (e.g., media and policymakers) with the goal of improving their perception of the experience and benefits of selling in our store. As Sr. Economist for SER, you will build the science models and the supporting structures needed to analyze, dive deep, and develop underlying data for our external messaging and reports. Your work will demonstrate how Amazon’s innovation enables seller success and supports their growth. You will have the opportunity to present findings to cross functional team partners to drive improvements. You will work closely with other Applied/Research/Data Scientists, Economists, Data Engineers, Software Development Engineers, Program Managers and Business Partners to solve challenging problems. You need be comfortable using intellect, curiosity and technical ability to develop innovative solutions to business problems. You will become an expert on aspects of the business to understand how to apply science and analytics to develop and substantiate our strategic messaging. You'll thrive if you enjoy tackling ambiguous challenges using the economics toolkit to identify and solve problems at scale. You will be expected to provide clear and concise explanation of results and approaches, provide opinion and guidance on problem solving, and present to senior leadership across multiple business units. The ideal candidate will have outstanding leadership skills, proven ability to develop, enhance, automate, and manage science models from end to end. The ideal candidate will have strong data mining and modeling skills and will be comfortable facilitating idea creation and working from concept through to execution. The ideal candidate must have demonstrated ability to manage medium-scale automation and modeling projects, identify requirements and build methodology and tools that are mathematically grounded but also explainable operationally, apply technical skills allowing the models to adapt to changing attributes. Key job responsibilities • Contribute to external messaging and report strategy based on science models and data analysis • Develop models to measure long term impact of the relationship between Amazon selling tools and seller behavior • Collaborate with product and engineering teams across the business to develop comprehensive models with diverse inputs • Use economical techniques to discover and develop underlying data for our strategic messaging and report work • Research, experiment and implement novel approaches. • Work closely with other scientists across teams. • Use the best practices in science: data integrity, design, test, and implementation and documentation. About the team SER is functionally diverse organization that includes a broad spectrum of skillsets and job families. We are a team of program and product managers, business analysts, creatives, storytellers, and small business evangelists - all important contributors to our work in ensuring that perception of Amazon’s support of small business matches reality. We have a supportive, fast-paced team culture, and we prioritize learning, growth, and helping each other to continuously raise the bar. Your direct team would be comprised of product and program managers, business analysts, and data engineers, with stakeholders and partners in similar functions accross the organization.

Amazon's Search Science and AI team creates ML algorithms that connect customers around the world with products that delight them. We harness cutting-edge ML at Amazon's scale to make the customer experience easier and smoother. Our impact is large. For example, if your innovations save even 1 minute per customer per year, then for every 100 million customers, you save approximately 190 years of human effort. Key job responsibilities You will build search ranking systems that work for thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. As an Applied Scientist you will find the next set of big improvements to ranking, leverage large datasets to understand the complexities of customer behavior, and get your hands dirty by building ML models that work at Amazon scale. In addition to typical topics in ranking, we are particularly interested in exploration techniques and reinforcement learning. A day in the life Our primary focus is improving search ranking systems. On a day-to-day this means building ML models, analyzing data from your recent A/B tests, and guiding teams on best practices. You will also find yourself in meetings with business and tech leaders at Amazon communicating your next big initiative. About the team We are a team consisting of ML scientists and software engineers. Our interests and activities span machine learning for better ranking, statistics for better decision making, and infrastructure to make it all happen at scale and efficiently.

Amazon’s maps play a crucial role in our vehicle navigation, routing, and planning problems to ensure fast and safe deliveries to our customers. As part of the Last Mile Geospatial Science organization, you’ll partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting problem area to augment the maps and routing inputs from satellite/aerial imagery and street videos by leveraging the latest computer vision and deep learning techniques. Key job responsibilities Successful candidates should have a deep knowledge (both theoretical and practical) of various machine learning algorithms for large scale computer vision problems, the ability to map models into production-worthy code, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big, long term problems. The applied scientist should be proficient with image and video analysis using machine learning, including designing architecture from scratch, modify existing loss functions, full model training, fine-tuning, and evaluating the latest deep learning models. The Applied Scientist optimizes different models for specific platforms, including edge devices with restricted resources. Multi-modal models, e.g., Large Vision Language Models (LVLM), zero-shot, few-shot, and semi-supervised learning paradigms are used extensively. A day in the life 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! 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. The Search Ranking and Interleaving (R&I) team within the Marketplace Intelligence org in SP is responsible for determining which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the ranking and allocation of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. We are looking for an Applied Scientist to join the Search Ranking team in MI. The team is responsible for improving the quality of ads shown to users (e.g., relevance, personalized and contextualized ranking to improve shopper experience and business metrics) via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify big opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. Key job responsibilities - Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them - Research new and innovative machine learning approaches. - Recruit Scientists to the team and provide mentorship.

Do you want to be part of a team that's revolutionizing Amazon's fulfillment and packaging technology? Can you commit to optimizing systems that process tens-of-millions of customer packages daily with the lowest cost to serve and a defect-free customer experience? Do you have a passion for solving complex science challenges and building a sustainable e-commerce experience? Your Deep Learning skills can help make that a reality on the Mechatronics and Sustainable Packaging team. We are looking for a talented Senior Applied Scientist to join our team. You will join a team of Scientists and Machine Learning Engineers (MLEs) to directly improve the packaging experience of Amazon customers in the fulfillment technology space. The team consists of experts in the field of Statistics, Operational Research, Machine Learning (ML), Computer Vision and Generative AI who work together to break new ground in the world of automated packaging solutions. You'll work in a collaborative environment where you will deploy and scale ML solutions to peta-bytes of data, work on problems that haven’t been solved before, and understand whether they succeed via statistically relevant experiments across millions of customers. You'll see the work you do directly improve the packaging experience of Amazon customers in the fulfillment technology space. If you are interested in robotics, computer vision, machine learning, big data, and building scalable solutions, this role is for you. The successful candidate will have a PhD in Computer Science, Statistics, or Engineering with a strong focus on data analysis, machine learning, generative AI or a related field, and 5+ years of practical experience solving complex problems in computer vision, anomaly detection, robotics. or multi-modal classification systems. Manufacturing, packaging and/or logistic experience is a plus, but not a requirement. Key job responsibilities - Advance exploratory research projects in machine learning, statistics and related fields to create defect-free packaging customer experiences - Analyze large amounts of Amazon shipments to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities - Validate new or improved models via statistically relevant experiments across millions of customers - Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale

AMZL Global Fleet and Products (GFP) organization is responsible for fleet programs and capacity for Last Mile deliveries. The Fleet Planning team is looking for a Data Scientist to drive the most efficient use of fleet. Last Mile fleet planning is a complex resource allocation problem. The goal of fleet allocation planning is to optimize the size and mix of fleet allocated to DSPs through various programs to improve branded fleet utilization. Changes in routes, last mile network, exiting DSPs and new DSP onboarding create continuous need for re-allocation of fleet to maintain an efficient network capacity. This requires allocation to adhere to various operational limits (repair network, EV range, Station Charging capability) and also match route’s cube need to vehicles capacity. As a Data Scientist on the Fleet Planning team (GFP), you will be responsible for building new science models (linear programs, statistical and ML models) and enhancing existing models for changing business needs. You would work with program managers in planning, procurement, redeployment, deployment, remarketing, variable fleet and infrastructure programs to build models that would support the requirements of all programs in a coherent plan. Key job responsibilities • Build models and automation for planners for generating vehicle allocation plans • Partner with program teams to test and measure success of implemented model • Lead reviews with senior leadership, deep dive model outputs and explain implications of model recommendations.

About Amazon Health Amazon Health’s mission is to make it dramatically easier for customers to access the healthcare products and services they need to get and stay healthy. Towards this mission, we (Health Storefront and Shared Tech) are building the technology, products and services, that help customers find, buy, and engage with the healthcare solutions they need. Job summary We are seeking an exceptional Senior Applied Scientist to join a team of experts in the field of machine learning, and work together to break new ground in the world of healthcare to make personalized and empathetic care accessible, convenient, and cost-effective. We leverage and train state-of-the-art large-language-models (LLMs) and develop entirely new experiences to help customers find the right products and services to address their health needs. We work on machine learning problems for intent detection, dialogue systems, and information retrieval. You will work in a highly collaborative environment where you can pursue both near-term productization opportunities to make immediate, meaningful customer impacts while pursuing ambitious, long-term research. You will work on hard science problems that have not been solved before, conduct rapid prototyping to validate your hypothesis, and deploy your algorithmic ideas at scale. You will get the opportunity to pursue work that makes people's lives better and pushes the envelop of science. #everydaybetter Key job responsibilities - Translate product and CX requirements into science metrics and rigorous testing methodologies. - Invent and develop scalable methodologies to evaluate LLM outputs against metrics and guardrails. - Design and implement the best-in-class semantic retrieval system by creating high-quality knowledge base and optimizing embedding models and similarity measures. - Conduct tuning, training, and optimization of LLMs to achieve a compelling CX while reducing operational cost to be scalable. A day in the life In a fast-paced innovation environment, you work closely with product, UX, and business teams to understand customer's challenges. You translate product and business requirements into science problems. You dive deep into challenging science problems, enabling entirely new ML and LLM-driven customer experiences. You identify hypothesis and conduct rapid prototyping to learn quickly. You develop and deploy models at scale to pursue productizations. You mentor junior science team members and help influence our org in scientific best practices. About the team We are the Health AI team at HST (Health Store and Technology). The team consists of exceptional ML Scientists with diverse background in healthcare, robotics, customer analytics, and communication. We are committed to building and deploying the most advanced scientific capabilities and solutions for the products and services at Amazon Health.