Careers

Alexa International Tech (AIT) team is looking for a passionate and outcome-driven Applied Science Manager to lead the science team behind some of Alexa’s industry-leading technology. The team is focused on delivering exceptional digital personal assistance (Alexa+ ) experience for international customers using state of the art techniques in Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Science Manager in AIT you will: * Lead and grow a team of Applied Scientists working to develop and evolve Large Language models powering Alexa+ for international locales and languages * Define and execute on science roadmap for the team, working backwards from customer needs, business priorities and science capabilities * Drive the development of scalable multi-lingual NLP and LLM solutions and develop frameworks to evaluate various aspects of LLM performance * Partner with engineering teams to implement production-ready scientific solutions * Recruit, mentor, and develop top scientific talent * Foster a culture of innovation while maintaining high scientific standards * Review and provide guidance on experimental design, methodology, implementation and continuous improvement of science solutions * Collaborate with product, engineering, and business teams to continuously improve customer experience A day in the life * Review key metrics, goals and project progress from your team * Provide direction and guidance to your team, and unblock or coarse correct as need be, to ensure science solutions are on track and delivered with high quality. * Actively participate and contribute to launch readiness forums, leadership reviews and technical design reviews. * Meet with product managers to align on roadmap priorities and scientific requirements * Provide technical mentorship to team members on their projects * Review and give feedback on scientific documentation and methodology * Collaborate with engineering partners on implementation approaches * Lead team meetings focused on scientific innovation and best practices * Contribute through industry first research to drive the innovation forward. * Engage with the broader Amazon science community to share learnings and stay current with latest developments

Amazon Web Services (AWS) is looking for a Principal Applied Scientist to join the Amazon Q team. Q is AWS’s enterprise generative AI assistant that helps users answer questions, summarize documents, generate content, take actions, and automate workflows using information across enterprise systems. As a key member of this team, you will lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive. You’ll work on building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains. This role blends science leadership, hands-on innovation, and deep collaboration with engineering teams to bring research into production. Key job responsibilities Lead the design and development of foundational models and intelligent agent architectures tailored to enterprise use cases. Partner with engineering, product, and UX teams to integrate generative AI into the Amazon Q assistant. Drive experimentation to improve model accuracy, safety, latency, and cost efficiency. Mentor other scientists and engineers, helping raise the technical bar across the team. Contribute to and publish in top-tier conferences or file patents based on novel research contributions. About the team Amazon Q is a generative AI-powered assistant that helps employees become more productive by answering questions, generating content, summarizing data, and automating workflows using enterprise information. Our team builds the intelligence behind Q, leveraging large language models, retrieval-augmented generation, and agentic architectures to orchestrate complex workflows securely and at scale. We’re focused on building systems that reason, plan, and act across multiple modalities and business tools. As part of AWS’s broader Agentic AI initiative, we’re shaping the future of enterprise AI—empowering organizations to solve problems faster, reduce operational overhead, and unlock new levels of efficiency and decision-making.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The 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. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As a Senior Applied Scientist on this team, you will: - Be the technical leader in Machine Learning; lead efforts within this team and across other teams. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE

The Customer Engagement Technology (CET) team leads AI/LLM- (Large Language Model) driven customer experience transformation using task-oriented dialog systems. We develop multi-modal, multi-turn, goal-oriented dialog systems that can handle customer issues at Amazon scale across multiple languages. These systems are designed to adapt to changing company policies and invoke correct APIs to automate solutions to customer problems. Additionally, we enhance associate productivity through response/action recommendation, summarization to capture conversation context succinctly, retrieving precise information from documents to provide useful information to the agent, and machine translation to facilitate smoother conversations when the customer and agent speak different languages. Key focus areas include: - Task-Oriented Dialog Systems: Building reliable, scalable, and adaptive LLM-based agents for understanding intents, determining eligibilities, making API calls, confirming outcomes, and exploring alternatives across hundreds of customer service intents, while adapting to changing policies. - Lifelong Learning: Researching continuous learning approaches for injecting new domain knowledge while retaining the model's foundational abilities and preventing catastrophic forgetting. - Agentic Systems: Developing a modular agentic framework to handle multi-domain conversations through appropriate system abstractions. - Complex Multi-turn Instruction Following: Identifying approaches to guarantee compliance with instructions that specify standard operating procedures for handling multi-turn complex scenarios. - Inference-Time Adaptability: Researching inference-time scaling methods and improving in-context learning abilities of custom models to enable real-time adaptability to new features, actions, or bug fixes without solely relying on retraining. - Context Adherence: Exploring methods to ground responses in specific customer attributes, account information, and behavioral data to prevent hallucinations and ensure high-fidelity responses. - Policy Grounding: Investigating techniques to align bot behavior with evolving company policies by grounding on complex, unstructured policy documents, ensuring consistent and compliant actions. - End-to-End Dialog Policy Optimization: Researching alignment approaches to optimize successful dialog completions. - Scalable Evaluations: Developing automated approaches to evaluate the quality of experience, and correctness of agentic resolutions. Key job responsibilities - Research and development of LLM-based chatbots and conversational AI systems for customer service applications. - Design and implement state-of-the-art NLP and ML models for tasks such as language understanding, dialogue management, and response generation. - Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate LLM-based solutions into Amazon's customer service platforms. - Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of chatbot systems. - Conduct experiments and evaluations to measure the performance of developed models and systems to identify areas for improvement. - Stay up-to-date with the latest advancements in NLP, LLMs, and conversational AI, and explore opportunities to incorporate new techniques and technologies into Amazon's customer service solutions. - Collaborate with internal and external research communities, participate in conferences and publications, and contribute to the advancement of the field. A day in the life We thrive on solving challenging problems to innovate for our customers. By pushing the boundaries of technology, we create unparalleled experiences that enable us to adapt rapidly in a dynamic environment. Our decisions are guided by data, and we collaborate with engineering, science, and product teams to foster an innovative learning environment. 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! Benefits Summary: 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 About the team Join our team of scientists and engineers who develop and deploy LLM-based Conversational AI systems to enhance Amazon's customer service experience and effectiveness. We work on innovative solutions that help customers solve their issues and get their questions answered efficiently, as well as associate-facing products that support our customer service associate workforce.

Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading technology in generative AI and foundational models. As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in generative AI. Your work will directly impact millions of our customers in the form of products and services that make use of speech, vision and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, image and structured data sources, and large-scale computing resources to accelerate advances in machine learning and foundation models. More specifically, you will have the opportunity to impact millions of our customers by researching and building innovative solutions using Agentic AI. Agentic AI drives innovation at the forefront of artificial intelligence, enabling customers to transform their businesses through cutting-edge generative AI solutions. We build and deliver the foundational AI services that power the future of cloud computing, helping organizations harness the potential of AI to solve their most complex challenges. Join our dynamic team of AI/ML practitioners, applied scientists, software engineers, and solution architects who work backwards from customer needs to create groundbreaking technologies. If you're passionate about shaping the future of AI while making a meaningful impact for customers worldwide, we want to hear from you. A day in the life A day in the life Diverse Experiences AWS 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 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.

As a Senior Applied Scientist for Last Hundred Yards Automation, you will be at the forefront of developing cutting-edge AI and ML solutions that power our autonomous delivery robots. This role combines deep expertise in machine learning, computer vision, and robotics to solve complex challenges in real-world autonomous navigation, obstacle detection, and dynamic path planning. You will work closely with robotics engineers, software developers, and product teams to research, design, and implement sophisticated algorithms that enable safe and efficient last-yard delivery operations. This position requires a unique blend of theoretical knowledge and practical implementation skills, with a focus on transforming research concepts into production-ready solutions that can operate reliably in diverse real-world environments. Key job responsibilities The Senior Applied Scientist will lead the development and implementation of advanced AI/ML models for autonomous navigation, focusing on critical areas such as real-time object detection, semantic scene understanding, and predictive movement planning. You will be responsible for designing and conducting experiments, analyzing complex datasets, and developing novel algorithms to improve robot performance and reliability. The role involves collaborating with cross-functional teams to integrate ML solutions into the robotics platform, optimizing model performance for edge computing, and establishing metrics for continuous improvement. You will also mentor junior scientists, contribute to research publications, and stay current with latest developments in the field. Key activities include developing robust ML pipelines, implementing state-of-the-art deep learning architectures, and creating innovative solutions for challenging problems such as adverse weather navigation, multi-robot coordination, and human-robot interaction. Success in this role requires balancing research excellence with practical implementation, while maintaining a strong focus on safety and reliability in autonomous systems. About the team LMDA (Last Mile Delivery Automation) is a cutting-edge automation system designed to revolutionize the final phase of package delivery, focusing specifically on the last hundred yards from delivery vehicle to customer doorstep. The system integrates autonomous robots equipped with advanced AI/ML algorithms, real-time navigation capabilities, and smart obstacle avoidance technology to efficiently transport packages to their final destination. These robots are designed to navigate various terrains, including sidewalks, building entrances, and residential complexes, while adhering to local safety regulations and maintaining secure delivery protocols. LMDA incorporates cloud-based fleet management, real-time tracking, and dynamic route optimization to ensure seamless coordination between delivery vehicles and robots. The system features a user-friendly interface for both delivery personnel and customers, providing live tracking, delivery notifications, and interactive robot communication. As labor costs rise and delivery volumes increase, LMDA represents a scalable, cost-effective solution that addresses the growing challenges of last-mile delivery while significantly improving operational efficiency and customer satisfaction.

Are you seeking an environment where you can drive innovation? Do you want to apply inference, advanced statistical modeling and techniques to solve world's most challenging problems in? Do you want to play a crucial role in the future of Amazon's Retail business? Do you want to be a part of a journey that develops a new technology from scratch for answering critical business question in Amazon Retail? Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize acquisition, enable a number of purchase options, ensure great , store products so they are available for fast delivery, and minimize package frustration. The Technology (SCOT) Group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it. The Consumer Instock Value (CIV) team within Amazon's Supply Chain Optimization Technology (SCOT) Group develops and manages systems that estimate the long-term impact of inventory availability and delivery speed changes at the product level. Our estimates are crucial inputs for multiple production systems across Amazon's supply chain planning, helping teams make critical decisions about inventory management, selection, and placement. Key responsibilities of a Data Scientist in CIV Team include: - Developing new statistical, causal, and machine learning techniques and develop solution prototypes to drive innovation - Working with technical and non-technical customers to design model improvements and communicate results - Collaborating with our dedicated software team to create production implementations for large-scale data analysis - Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business - Presenting research results to our internal research community - Leading training and informational sessions on our science and capabilities - Your contributions will be seen and recognized broadly within Amazon, contributing to the Amazon research corpus and patent portfolio.

Are you a AI-focused data scientist that wants to think fast, dive deep, and build? We are seeking a data scientists to join our the Amazon Nova Solutions team to work with Foundation Model (FM) teams alongside internal and external Generative AI customers to customize foundation models and build the applications of the future. In this role, you will be the voice of the builder, with ownership for working alongside Amazon businesses to develop GenAI solutions that deliver their immediate business value and realize the future of customer experience. You will help your customers understand best practices and develop new best-practice patterns as you encounter new problem spaces. With your day to day focus on the builder, you will be expected to provide insights to product, science, and engineering on how customers build with our FM's and how we can improve. You will be empowered to invent on behalf of our customers and advocate for their needs throughout design, build, and launch of next gen FM's. You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, and fine-tuning techniques. You should be proficient with design, deployment, and evaluation of foundation models - including evaluation dataset design, metric design, implementation, failure analysis. Ideal candidate has hands on experience with both AI Frameworks (e.g. Strands, LangGraph, CrewAI, DSPy) and model customization tools including PyTorch, TensorBoard, MLFlow/W&B, Bedrock, and SageMaker This position has a high level of visibility, so you will need to be able to communicate clearly and compellingly at all levels of the company. Additionally, you will be responsible for measuring business impact by diving deep into metrics and customer inputs. You should have a positive attitude and strong work ethic; you will work fast and smart, and adapt and iterate quickly. Key job responsibilities - Customer Advisor- Implement, and deploy state of the art machine learning solutions under Gen AI. You will build prototypes, PoCs, and explore new solutions. You will interact closely with our customers. - Thought Leadership – Evangelize AWS GenAI services and share best practices through forums such as AWS blogs, white-papers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc. - Ensure success in designing, building and migrating applications, software, and services with Amazon Nova FM's, AWS Bedrock, and other AWS GenAI capabilities. - Interact with customers directly to understand their business problems and help them implement durable GenAI systems. - Educate customers on the value proposition of amazon Nova FM ecosystem and showcase the art of possible. - Drive evaluation strategy, metric design, and evaluation data design for real-world use cases - Drive deep architectural discussions and design exercises to create solutions built with Amazon Nova FM's - Author and contribute to AWS customer-facing publications such as whitepapers, workshops, demos and proof of concepts. - Build deep relationships with customer engineering, product, and science leaders. About the team Amazon Nova Applied AI Solutions Team serves as a bridge between Foundation Model science, engineering, and product teams and the internal Amazon teams using these models to deliver business value to Amazon's customers. We help customers build GenerativeAI applications and apply the learning from these engagemetns to help make the Foundation Models better every day.

Amazon is seeking an exceptional Applied Scientist to join AGI Info Content team. In this role, you will be at the forefront of developing and enhancing the intelligence of AmazonBot crawler and content processing. The team is a key enabler of Amazon's AGI initiatives such as data pipelines for Olympus model training and collecting data for AGI Info grounding services. Our systems operate on web scale. This requires great combination of innovation to utilize all SOTA ML techniques in combination with model optimization to operate on 100k+ requests/decision per second. Your work will directly impact the quality and efficiency of our data acquisition efforts, ultimately benefiting millions of customers worldwide. Key job responsibilities - Design, develop, and implement advanced algorithms and machine learning models to improve the intelligence and effectiveness of our web crawler and content processing pipelines. - Collaborate with cross-functional teams to identify and prioritize crawling targets, ensuring alignment with business objectives - Analyze and optimize crawling strategies to maximize coverage, freshness, and quality of acquired data while minimizing operational costs as well as dive deep into data to select the highest quality data for LLM model training and grounding. - Conduct in-depth research to stay at the forefront of web acquisition and processing. - Develop and maintain scalable, fault-tolerant systems to handle the vast scale of Amazon's web crawling operations - Monitor and analyze performance metrics, identifying opportunities for improvement and implementing data-driven optimizations - Mentor and guide junior team members, fostering a culture of innovation and continuous learning

Do you want to join an innovative team of scientists, engineers and technologists who use AI and machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build agentic systems that transform the business landscape, powered by state-of-the-art AI, such as Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating advanced algorithms to solve real world problems? Do you like to build end-to-end business solutions, central to Amazon's tech strategy, that directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Seller Experience Innovation team. Key job responsibilities - Use AI, machine learning and statistical techniques to create the next generation of the agentic tools that empower Amazon's Selling Partners to succeed. - Design, develop and deploy highly innovative AI models to interact with Sellers and delight them with solutions. - Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Research and implement novel machine learning and statistical approaches. - Lead strategic initiatives to employ the most recent advances in AI in a fast-paced, experimental environment. - Drive the vision and roadmap for how ML can continually improve Selling Partner experience. - Represent the team as a science leader across Amazon and in public settings. About the team Seller Experience Innovation is a growing team of scientists, engineers and technologists engaged in the research and development of the next generation of AI-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from foundation models and LLMS to Deep Learning to Computer Vision to Reinforcement Learning, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.