Careers

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. Our models learn from customer behavior to answer questions the business can't test directly and use those answers to guide where Prime Video invests in content and product. We're looking for an Applied Scientist to help build the next generation of these models. As an Applied Scientist on this team, you will build machine learning and deep learning models on large-scale global data to simulate customer behavior across the business. Ideally you bring experience with reinforcement learning, causal inference, and/or the design of agentic AI systems. You will partner closely with business, finance, engineering, and science stakeholders to take ideas from concept and prototype through to production. The candidate should have strong communication skills and the ability to translate data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Build models that simulate customer behavior to answer counterfactual "what-if" questions that guide major content and product investment decisions. • Apply deep learning, reinforcement learning, causal inference, and experimental design to large-scale customer data to model how customers respond to change. • Design and prototype agentic AI systems and other novel ML approaches and research new methods to improve the accuracy and scale of our models. • Validate and calibrate models against real-world randomized experiments, and partner with software engineers to deliver scalable, production-ready systems. • Translate model outputs into clear recommendations and communicate results to business, finance, and science stakeholders through both technical papers and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music The Data, Insights, Science and Optimization, Music Product and Tech (DISCO MPT) team is looking for a Applied Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across product, science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment. Key job responsibilities • Lead the research and development of models and science products powering personalized recommendations • Partner with product leaders at Amazon Music to develop science-driven business strategies • Partner with science, marketing and product teams across Amazon Entertainment and subscription businesses • Educate internal teams on analytics, insights and measurement • Develop models to determine drivers of key performance metrics, and automate the process of deep diving into variances • Collaborate with product and engineering teams to evaluate the impact of new features or algorithms (e.g., the Playlist Song Recommendation experiments) • Analyze the results of experiments and provide recommendations to optimize solutions • Partner with Data Engineers to develop new product based on GenAI technology • Write high quality production code About the team The DISCO team focuses on accelerating Amazon Music customer growth by empowering product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, self-service analytics, insights and predictive models enabling acquisition, engagement and retention at scale with personalized customer touchpoints.

We are looking for a Principal Applied Scientist to own and advance the scientific vision for WorkSpaces Advisor — our agentic AI system that serves as an always-on troubleshooting companion for workspace administrators and end users. You will define the technical roadmap that transforms Advisor from a recommendation engine into a fully autonomous agent capable of reasoning across complex system states, orchestrating multi-step remediation workflows, and continuously learning from outcomes. This is a leadership role requiring someone who can set the scientific direction for agentic AI in the troubleshooting domain, drive breakthroughs in reasoning and planning under uncertainty, and build the ML foundations that make Advisor the most trusted AI companion in enterprise workspace management. You'll define and drive the scientific strategy for Advisor's agentic capabilities, establishing the research agenda that keeps us at the frontier of autonomous troubleshooting and self-healing systems. Architect agentic reasoning systems that enable Advisor to autonomously diagnose root causes across complex, multi-signal environments — correlating performance telemetry, session behavior, network conditions, and infrastructure state to identify problems before users feel them. Design and build planning and orchestration frameworks that allow Advisor to compose multi-step remediation actions, reason about dependencies and risks, and execute recovery workflows with appropriate human-in-the-loop guardrails. Develop advanced causal inference models that move beyond correlation to true root-cause identification, enabling Advisor to distinguish symptoms from underlying issues across interconnected system layers. Build continuous learning systems where Advisor improves from every interaction — leveraging reinforcement learning from human feedback (RLHF), outcome-driven reward signals, and retrieval-augmented generation (RAG) to expand its troubleshooting knowledge over time. Pioneer natural language reasoning capabilities that allow Advisor to explain its diagnostic process, communicate findings clearly to administrators, and engage in collaborative problem-solving dialogue. Establish evaluation frameworks and safety mechanisms that ensure Advisor's autonomous actions maintain customer trust — defining confidence thresholds, escalation policies, and rollback strategies for automated remediation. Influence the broader organization's AI strategy by identifying opportunities to extend Advisor's agentic patterns to adjacent problem spaces, and by publishing findings that advance the state of the art in autonomous IT operations. Key job responsibilities - Set the scientific vision and long-term research agenda: Define what "best-in-class agentic troubleshooting" looks like scientifically, identify the key unsolved problems, and chart a multi-year path to solving them — securing buy-in from VP-level leadership. - Deliver breakthrough solutions on highly ambiguous problems: Independently identify, frame, and solve novel research challenges in agentic AI for troubleshooting — problems where neither the approach nor the success criteria are pre-defined. - Influence and align across the organization: Drive scientific alignment across product, engineering, and business teams. Translate complex ML concepts into actionable product strategy. Represent the science team in leadership forums and planning cycles. - Build and elevate scientific excellence: Mentor scientists and engineers across the team. Establish best practices for experimentation, evaluation, and deployment of agentic systems. Set the standard for scientific rigor and code quality. - Deliver end-to-end production systems with outsized business impact: Own the full lifecycle from research to deployment for Advisor's core intelligence — making pragmatic trade-offs between long-term invention and near-term delivery while ensuring measurable customer and business outcomes. - Advance the state of the art: Contribute to the external scientific community through publications, patents, and engagement that positions AWS as a leader in autonomous AI operations — bringing outside-in innovation back into Advisor. About the team AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges — blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale. Within this organization, we are building the next generation of secure, intelligent workspaces — environments purpose-built for human-AI collaboration at enterprise scale. Our WorkSpaces Advisor is an AI-powered troubleshooting companion that proactively detects, diagnoses, and resolves workspace issues, transforming reactive IT support into intelligent, autonomous problem-solving.

Amazon Industrial Robotics is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon Industrial Robotics, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Applied Scientist in Robot Perception, you will be at the forefront of this transformation. You will develop and deploy state-of-the-art perception algorithms that enable robots to truly understand and interact with the physical world — bridging the gap between theoretical research and realworld impact. Bringing deep expertise in Computer Vision and a nuanced understanding of the capabilities and limitations of modern Vision-Language Models (VLMs), you will innovate boldly and push the boundaries of what's possible. Our vision for the Perception layer is ambitious: to enable seamless, intelligent interaction between the user, the robot, and its environment. This is a rare opportunity to work at the intersection of deep learning, large language models, and robotics — contributing to research that doesn't just advance the field, but reshapes it. You will collaborate with world-class teams pioneering breakthroughs in dexterous manipulation, locomotion, and humanrobot interaction, all at an unprecedented scale. Key job responsibilities Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding • Lead research initiatives in computer vision, sensor fusion and 3D perception • Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities • Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment • Mentor junior scientists and engineers; contribute to a culture of technical excellence • Define and track key metrics to measure perception system performance in real-world environments • Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment • Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations • Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team • Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our Industrial Robotics Group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.

Amazon is looking for an Applied Scientist to help build the next generation of sourcing and vendor experience systems. The Optimal Sourcing Systems (OSS) owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units. Our goals are to increase reliable access to supply, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon. As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. Some critical research areas in our space include modeling buying decisions under high uncertainty, vendors' behavior and incentives, supply risk and enhancing visibility and reliability of inbound signals. Key job responsibilities - Set the scientific strategic vision for the team. Lead problem decomposition and roadmap development. - Identify and frame research challenges in ambiguous problem areas; invent novel methodologies to address them. Distinguish between problems requiring novel solutions versus those addressable with existing approaches. - Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs. - Set an example with exemplary scientific analyses; maintainable, well-tested code; and simple, effective solutions. - Drive the design of scientifically-complex software solutions, personally writing critical-path code that embodies scientific novelty. Deploy novel models into production with a track record of impactful delivery. - Develop reusable science components that resolve architecture deficiencies; set standards and drive adoption of state-of-the-art techniques. - Influence team business and engineering strategies. - Communicate effectively with stakeholders to drive alignment and build consensus. - Foster collaborations between scientists across Amazon researching similar problems. Proactively resolve endemic issues where the team's technologies bottleneck other teams. - Actively engage in the development of others, both within and outside the team. - Participate in the science hiring process and engage with the broader scientific community through publications, presentations, and patents.

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 7 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.

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 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 Computer Vision, 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. Key job responsibilities -

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.