Careers

As part of the Alexa AI team, our mission is to provide scalable and reliable evaluation of state-of-the-art Conversational AI. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of Large Language Models (LLMs), Artificial Intelligence (AI), and Natural Language Processing (NLP) to invent and build the end-to-end evaluation of how customers perceive state-of-the-art, context-aware conversational AI assistants. A successful candidate will have a strong machine learning background, a deep understanding of the conversational AI stack, and a desire to push the envelope in conversational-AI evaluation. As a senior member of the team, you will own ambiguous, high-impact evaluation problems end-to-end — from defining the scientific direction to shipping the models and metrics that millions of customers and the developers who build for them depend on. The ideal candidate has hands-on experience building Generative AI solutions with LLMs, including LLM-as-a-Judge (LLMaaJ), model distillation, Supervised Fine-Tuning (SFT), In-Context Learning (ICL), and Learning from Human Feedback (LHF). As an Applied Scientist, you will leverage your technical expertise to set the research agenda for how we measure conversational quality, mentor other scientists and engineers, and partner across science, engineering, and business teams to research and develop novel evaluation methods. You will analyze and understand customer experiences using Amazon's heterogeneous data sources, and design, train, and maintain the evaluation models that serve as the source of truth for assistant quality. Key job responsibilities * Own the design, development, and long-term maintenance of flagship quality-evaluation metrics for a state-of-the-art conversational assistant — spanning ground-truth definition, data preparation, model training, and production maintenance. * Research and build LLM-based evaluators, including LLM-as-a-Judge systems, and distill large judge models into efficient, cost-effective models suitable for scaled online use. * Set the technical direction for evaluation science and raise the bar for scientific rigor across the team; mentor scientists and engineers and review their work. * Ensure data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground-truth generation, normalization, and transformation. * Present proposals and results to partner teams and leadership in a clear manner, backed by data and coupled with actionable conclusions. * Partner with engineers to develop efficient data-querying and inference infrastructure for both offline and online use cases. About the team Central Analytics and Research Science (CARS) is an analytics, software, and science team within Amazon's Alexa AI organization. Our mission is to provide an end-to-end understanding of how customers perceive the assistants they interact with – from the metrics themselves to software applications to deep dive on those metrics – allowing assistant developers to improve their services. Learn more about Amazon’s approach to customer-obsessed science on the Amazon Science website, which features the latest news and research from scientists across the company. For the latest updates, subscribe to the monthly newsletter, and follow the @AmazonScience handle and #AmazonScience hashtag on LinkedIn, Twitter, Facebook, Instagram, and YouTube.

Amazon.com’s Product Detail Page team is looking for talented, motivated and passionate applied scientist to be part of the design and development of a highly scalable multi-tiered shopping application to provide the best possible online shopping experience for Amazon customers world-wide. Our team is comprised of talented applied scientists, developers, testers, program managers, designers and product managers tasked with the singular goal to create THE world's best buying experience. Scientists on this team develop the next-generation technologies and experiences that change how millions interact and shop online. To provide the best possible online shopping at the scale of the web requires ideas from every area of computer science, including distributed computing, large-scale system design, machine learning, natural language processing, data compression and user interface design; the list goes on and is growing every day. We need our scientists to be versatile and always eager to tackle new problems as we continue to push technology forward. Our team leverages sophisticated econometric, machine learning, and big data technologies to help customers to discover the right products at the right prices from millions of trusted sellers billions of times a day. If you are looking for a career-defining opportunity on one of the most customer centric and business impacting teams within Amazon, we’d love to hear from you. We are looking for an Applied Scientist to help build the next generation of Detail Page optimization algorithms. These new set of algorithms will incorporate the continually changing preferences of our customers and continue to scale with numerous new programs that Amazon is introducing for our customers. You will work with multiple Amazon businesses and programs to identify big business opportunities and propose new business features and technical systems to improve customer experience on Amazon Detail Page, Search Page and many other widgets throughout the website. You will be responsible for the quality of algorithm design and will get the opportunity to present your ideas and share results of your deliverables with Amazon executives on a frequent basis. You will get an opportunity to work with senior scientists to define and enforce broad, company-wide technical standards in optimization techniques, statistical modeling and simulation techniques, and/or data analytics.

We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology powering Alexa for Shopping, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey. This innovative role focuses on developing and optimizing language model-powered (LLM/SLM) conversational experiences. The core emphasis is to get the best performance out LLMs/SLMs via careful and methodical instruction design, contextual grounding, informed choices of MCP tools and agent/multi-agent systems, context engineering, model fine-tuning, evaluation frameworks, and experimentation to systematically improve quality, robustness, and customer impact. The work combines scientific rigor with product intuition to systematically raise the bar for conversational AI performance at Amazon scale. Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data-driven insights to continuously improve our systems. Key job responsibilities As an Applied Scientist on our team, you will develop and maintain LLM agents, including automated eval pipelines, LLM-as-a-judge methodologies, rubric design, and dataset curation to measure nuanced aspects of response quality. You will partner with the wider org to experiment with techniques such as retrieval augmentation, context enrichment, prompt decomposition, and model fine-tuning or post-training strategies, if and when applicable. Where latency and cost constraints demand it, you will lead post-training of small language models (SLMs) — including supervised fine-tuning, preference optimisation, and distillation — to deliver low-latency conversational and shopping experiences. You will apply applied machine learning and deep learning techniques as last-mile improvements to shopping experiences, that might span ranking, relevance, personalisation, and multimodal understanding. You will design and evaluate agentic architectures that balance the needs of diverse shopping use cases, making principled choices across paradigms such as single-agent and multi-agent systems, memory management strategies, and tool orchestration to optimise for quality, latency, and reliability at scale. You will leverage petabytes of data and identify opportunities to leverage machine learning models aimed at making conversational systems more performant. A day in the life - Perform hands-on analysis of large-scale multimodal interaction datasets to develop insights into how customers engage with conversational AI systems and how to improve response quality and customer experience. - Use statistical methods, experimentation, and data-driven analysis to develop scalable approaches for measuring, evaluating, and optimizing large language model (LLM)-based shopping assistant systems, leveraging structured and unstructured contextual signals. - Conduct deep-dive analyses to identify opportunities for improving conversational relevance, grounding, customer satisfaction, and downstream business impact. - Collaborate with Product management and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment. - Communicate results and insights to both technical and non-technical audiences, including through presentations, written reports, and data visualizations. About the team The Alexa for Shopping Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI-driven shopping experiences at Amazon. The team works on every aspect of the conversational AI system, from making it agentic, enabling customers to set price alerts or empower the assistant to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance. We utilize and advance state-of-art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer. You will be part of a team committed to pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors. This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work on scale. This position requires experience with developing CV, Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms. Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for computer vision applications. - Research and implement the state-of-the-art computer vision and Vision Language models algorithms. - Collaborate with product managers and engineering teams to design and implement computer vision and machine learning based features for Ring devices - Influence system design and product vision by making informed decisions on the selection of technology, data sources, algorithms, and sensors.

Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

We are seeking Data Scientist II with strong science application skills to join our Device Economics team. This role will focus primarily on Amazon's innovative devices and services (e.g. Echo Family of Devices), working at the intersection of economic modeling, forecasting science, and business strategy. The ideal candidate will be responsible for pre-launch forecasts, annualized overall forecasts, identifying substitution patterns, and partnering closely with product managers and marketing managers to understand the evolution of the Devices portfolio. Key job responsibilities Forecasting & Modeling 1. Develop and maintain pre-launch forecasts and annualized overall forecasts for Amazon Devices 2. Identify and model substitution patterns across the device portfolio 3. Build economic and financial models to support demand planning and business decisions 4. Formulate relevant analytical frameworks to address key economic issues in device forecasting Science Communication & Collaboration 1. Explain complex science models and methodologies to non-technical stakeholders including product managers and marketing managers 2. Collaborate with economists, data scientists, and applied scientists across Decision Science 3. Present results of analyses to cross-functional teams and leadership 4. Build trust in science models and forecast outputs with product teams Innovation & Strategic Thinking 1. Think creatively about ways that leading-edge analytics and emerging data sources can address Devices' most pressing business challenges 2. Help internal teams leverage analytic tools to better manage innovation 3. Conduct empirical studies and perform quantitative and qualitative research 4. Identify opportunities to improve forecasting accuracy and business impact Cross-Functional Partnership 1. Work closely with product managers and marketing managers to understand portfolio evolution and business strategy 2. Support DSO leadership in quarterly business reviews and strategic planning A day in the life Your days will be split between refining and building models and working with business leaders to interpret them. You own science-based forecasts that can directly impact Amazon's bottom line on the order of multi-million dollar decisions. - You will perform model refreshes or updates to analyses as needed; and, - You will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. About the team The Decision Science team within DSO (Device Supply Organization) is responsible for forecasting and demand planning initiatives across Amazon Devices. The DSO team of 300+ engineers, scientists, and PMs applies quantitative methods and data-driven approaches to replace judgment-based decisions with science-driven forecasts. Decision Science focuses on lifetime demand forecasting using econometric and machine learning models for rapid reforecasting, mix adjustments, and portfolio management for new product launches. We also inform to go/no-go investment decision for new product initiatives

Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer. As an Applied Scientist, you will work with talented peers pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors. This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work at scale. This position requires experience with developing Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms. Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for computer vision applications. - Research and implement the state-of-the-art computer vision and Vision Language models algorithms. - Collaborate with product managers and engineering teams to design and implement computer vision and machine learning based features for Ring devices - Influence system design and product vision by making informed decisions on the selection of technology, data sources, algorithms, and sensors.

As a Senior Applied Scientist in the Alexa AI team, you will define and drive the science roadmap for state-of-the-art conversational AI systems powered by large language models, directly impacting how millions of customers interact with Alexa daily. You'll lead the design of LLM fine-tuning, alignment, and agentic architectures that operate reliably at scale across many languages and devices, owning delivery from research formulation through production deployment. Working at the intersection of research and production, you'll translate the latest advances into customer-facing features. Your work will span the full ML lifecycle: developing novel evaluation frameworks, building automated training pipelines, and conducting rigorous experimentation across diverse devices and endpoints. Collaborating with engineering, product, and cross-functional science teams across Amazon, you'll tackle the team's most complex technical challenges while maintaining practical focus on customer value. This role offers the opportunity to publish at top-tier conferences, generate intellectual property, and see your innovations scale to one of the world's most popular voice assistants. Key job responsibilities As a Senior Applied Scientist in the Alexa AI team: - Define and drive the science roadmap for conversational AI capabilities powered by large language models - Design, implement, and evaluate novel approaches to LLM fine-tuning, alignment (RLHF, DPO, RLVR), and distillation for production deployment - Architect agentic systems (multi-step reasoning, tool use, planning, and orchestration) that work reliably at scale - Develop evaluation frameworks and methodologies that go beyond standard benchmarks to capture real-world conversational quality - Translate research advances into customer-facing products, working closely with engineering, product, and cross-functional science teams - Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance - Tackle the team's hardest technical problems while maintaining practical focus on customer value and solution generalizability - Advance the team's scientific reputation through high-impact publications and presentations at top-tier internal and external venues, and generate intellectual property through patents The applicable collective agreement for this role is CBA for employees of Telecommunication Sector. The position is classified at level 6 or above, depending on the candidate’s skills, competences and experience. The minimum gross annual base salary for this position is listed below. The base salary listed corresponds to working on a full-time basis. For part-time hours, the salary will be pro-rated. Amazon reserves the right to offer a higher salary and/or level, depending on the candidate's skills, competencies, and experience. Amazon's package may include a sign on payment. In addition, the candidate may be eligible to participate in a restricted stock unit scheme operated independently by Amazon.com Inc. in USA. Your recruiting team will share final salary and any restricted stock unit scheme if applicable, depending on skills and requirements. In addition to statutory benefits, and those applicable to the relevant CBA, company supplementary benefits may apply subject to further terms. Italy- EUR104,500 gross annually. A day in the life As a Senior Applied Scientist in the Alexa AI team, your day will involve leading cross-functional collaborations with engineering, product, and science teams to define the technical direction for our conversational assistant. You'll design experiments and review model results that shape the science roadmap, mentor junior scientists, and make high-judgment calls on architecture and deployment trade-offs. Working in a fast-paced, ambiguous environment, you'll own delivery of complex initiatives: from formulating novel research problems to presenting strategic recommendations to senior leadership. Your ability to influence across organizational boundaries will drive measurable customer impact while raising the bar for the experience of millions of customers. About the team Alexa AI is building the science and technology behind Alexa+, Amazon's next-generation conversational assistant. Our team works at the intersection of large language models, reinforcement learning from human feedback and verifiable rewards, agentic architectures, and multilingual/multimodal understanding. We operate at massive scale: our models serve customers across dozens of languages and device types. If you want to push the frontier of conversational AI and see your work used by people every day, come join us.

Are you excited about using econometrics to make multi-million dollar decisions more Science and Data Driven? Are you interested in supporting Consumer Hardware device concepts from innovative idea inception to launch? Do you want to work on a Economics and Data Science team focused on tackling some of the hardest business questions within the Devices business at Amazon and then scaling those Statistics and Econometrics solutions via internal to Amazon tools? Then this could be the role for you! The Decision Science team owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support analyses on hardware and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera subscriptions to the Amazon Smart Plug - all prior to launch. In this role, you will develop science for high visible senior leadership decisions on new devices and services and work with a cross-functional team to apply and scale innovative science broadly. Key job responsibilities - Design, estimate, and scale Berry-Levinsohn-Pakes (BLP) random coefficients demand models to quantify consumer heterogeneity, own- and cross-price elasticities, and substitution patterns across large product markets. - Implement and optimize numerical routines—including GMM estimation, contraction mappings, and simulation-based inversion—to solve structural demand systems at scale in Python. - Develop and validate instrumental variables strategies to address price endogeneity in differentiated product markets, ensuring unbiased and robust demand parameter estimates. - Build production-grade pipelines that ingest large-scale observational datasets, estimate consumer preferences, and generate product-level demand forecasts on recurring schedules. - Collaborate with cross-functional teams including product management, marketing, and operations to translate structural model outputs—such as willingness-to-pay and competitive diversion ratios—into actionable pricing and portfolio strategies. - Advance the team's structural modeling capabilities by researching and deploying extensions to classical BLP frameworks (e.g., supply-side estimation, dynamic demand, micro-moments) and documenting approaches in clear technical reports.

We're seeking a Senior Applied Scientist to pioneer sensor-based algorithms that power next-generation experiences across Amazon's device ecosystem, including Echo, Kindle, Fire TV, and Fire Tablets. Working with multidisciplinary teams of scientists and engineers, you'll develop innovative technologies at the intersection of signal processing and machine learning that transform how millions of customers interact with our products. The ideal candidate combines strong theoretical foundations in machine learning and signal processing with practical implementation skills. You'll develop state-of-the-art sensor algorithms from concept to production, translate complex research problems into practical consumer technologies, and create solutions optimized for diverse hardware platforms. We're looking for someone who thrives in fast-paced environments, solves complex problems efficiently, and iterates quickly based on real-world feedback. Your technical decisions will directly shape future product capabilities and deliver exceptional experiences to Amazon customers worldwide. Key job responsibilities - Develop and implement advanced algorithms and machine learning models to enhance Amazon's products and services. - Collaborate with cross-functional teams, including software engineers, scientists, and product managers to translate business needs into technical solutions. - Conduct thorough data analysis to identify trends, patterns, and insights that drive product innovation and improvement. - Optimize algorithms for performance, scalability, and efficiency across various Amazon platforms. - Present findings and recommendations to stakeholders, influencing product strategy and decision-making. - Stay abreast of the latest research and technological advancements in machine learning and related fields to continuously improve Amazon's offerings. - Mentor and guide junior scientists and engineers, fostering a culture of learning and innovation. - Ensure the ethical use of data and algorithms, adhering to Amazon's guidelines and best practices. - Contribute to the publication of research findings in conferences and journals, elevating Amazon's reputation in the scientific community. About the team At Amazon Lab126, we're a pioneering research and development hub dedicated to designing and engineering revolutionary consumer electronics. Established in 2004 as a subsidiary of Amazon.com, Inc., we've been at the forefront of innovation, starting with the creation of the best-selling Kindle family of products. Our portfolio has since expanded to include transformative devices such as Fire tablets, Fire TV, and Amazon Echo. Our Lab126 team is dedicated to developing advanced sensing technologies and algorithms, collaborating with program managers to design and implement transformative user features and experiences.