Careers

Are you excited about leveraging and extending state-of-the-art Deep Learning, Information Retrieval, Natural Language Processing, Computer Vision algorithms to solve customer problems at the scale of Amazon? As an Applied Scientist Intern, you will be working in the Melbourne office in a fast-paced, cross-disciplinary team of experienced R&D scientists. You will take on complex problems, work on solutions that leverage existing academic and industrial research, and utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even deliver these to production in customer facing products. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Deep Learning and Generative AI - Contribute to research that could significantly impact Amazon operations - Collaborate with a diverse team of experts in a fast-paced environment - Present your research findings to both technical and non-technical audiences - Collaborate with scientists on writing and submitting papers to top ML conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, ACL ICCV, CVPR, KDD. Key Opportunities: - Work in a team of ML scientists to solve applied science problems at the scale of Amazon - Access to Amazon services and hardware - Potentially deliver solutions to production in customer-facing applications - Opportunities to be hired full-time after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!

This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale We are seeking a Postdoctoral Scholar to join the SAF Lab. In this role, you will perform research around safe autonomy on highly dynamic robots, with a special focus on loco-manipulation and dynamically stable robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and robust whole-body control, layered safety filters that interface with learning modules, and the synthesis of CBFs from perception data and semantic information. You will push the boundaries of safe autonomy and validate your discoveries experimentally on the next generation of robotic platforms. The SAF lab provides a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon developing the next generation of safe autonomy, while also establishing strong connections with top academic research labs. Your research in the SAF lab will lay the foundations of safe learning on complex robots – removing bottlenecks to deployment and enable them to safely operate around humans. Key job responsibilities In this role you will: • Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains. • Develop the simulation and evaluation pipelines needed to run complex and large-scale validation of methods developed in high fidelity simulation environments. • Develop sim-to-real transfer pipelines that enable the deployment of simulation-based methods (controllers, policies) on hardware. • Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research. • Publish research at top-tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). A day in the life 0

We are seeking an Applied Scientist to help build Amazon’s next-generation customer memory and personalization systems. Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences. We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems. You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will collaborate closely with engineering and product teams to translate research into measurable customer impact. Please visit https://www.amazon.science for more information.

Come work with other scientists and software developers in Alexa Smart Home to design and build the next generation of Smart Home experiences using the latest multimodal Large Language Models (LLM's) and Computer Vision. We are evolving Alexa into an intelligent, indispensable companion that automates daily routines, simplifies interaction with appliances, electronics, and cameras and alerts when something unusual is detected. We are focused on making Alexa the user interface for the home, from the simplest voice commands (turn on the lights, turn down the heat) to use cases spanning home security, home entertainment, and the home environment. You can be part of a team delivering features that are highly anticipated by media and well received by our customers. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. As a Senior Applied Scientist, you will work with other scientists and software developers to design and build the next generation of Smart Home control using the latest Large Language Models and Vision Language Models. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. Key job responsibilities - Develop new inference and training techniques to improve the performance of LLM's for Smart Home control and Automation - Solve hard problems in computer vision, video summarization, and video search - Develop robust techniques for synthetic data generation for training large models and maintaining model generalization - Mentoring junior scientists to improve their skills, knowledge, and their ability to get things done About the team We are a team of Scientists, Machine Learning Engineers, and Software Developers that work together to make Alexa more insightful and proactive through ambient intelligence - with features like Alexa Hunches that automatically control Smart Home devices. We are interdisciplinary and we act like it -- we ask each other questions and value our different perspectives.

Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team! Key job responsibilities * Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. * Use SQL to query and analyze the data. * Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. * Use machine learning and analytical techniques to create scalable solutions for business problems. * Research and implement novel machine learning and statistical approaches. * Mentor interns. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. About the team When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. 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, and 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, and 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 Amazon values diverse experiences. Even if you do not meet all of the preferred 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. 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. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.

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.

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.