Careers

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team. The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation. Main responsibilities - Use statistical and machine learning techniques to create scalable risk management systems - Analyzing and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends - Design, development and evaluation of highly innovative models for risk management - Working closely with software engineering teams to drive real-time model implementations and new feature creations - Working closely with operations staff to optimize risk management operations - Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Tracking general business activity and providing clear, compelling management reporting on a regular basis - Research and implement novel machine learning and statistical approaches

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Come work for M13 - an AWS team specializing in the deception and disruption of cyber threats. We are looking for an Applied Scientist who is passionate about the security domain. You will build services and tools for security engineers and developers that leverage artificial intelligence and machine learning to pull unique insights about the cyber threat landscape. You will be part of a team building Large Language Model (LLM)-based services with the focus on enabling AWS teams to interact with our threat data. The team works in close collaboration with other AWS security services to power mitigations that protect the global AWS network and features in external security services such as Amazon GuardDuty, AWS WAF, and AWS Network Firewall. If you are excited about combating the ever evolving threat landscape then we'd love to talk to you. As an Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. Key job responsibilities • Understand the challenges that security engineers and developers face when building software today, and develop generalizable solutions. • Collaborate with the team to pave the way towards bringing your solution into production systems. Lead cross team projects and ensure technical blockers are resolved • Communicate and document your research via publishing papers in external scientific venues. About the team *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. *Diverse Experiences* 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. *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. *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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. *Mentorship and 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.

Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create? The Role: We are looking for a high caliber Applied Scientist to join our team. As part of the larger technology team working on new consumer technology, your work will have a large impact to hardware, internal software developers, ecosystem, and ultimately the lives of Amazon customers. In this role, you will: - Propose new research projects, get buy-in from stakeholders, plan and budget the project and lead the team for successful execution - Work closely with an inter-disciplinary product development team including outside partners to bring the prototype algorithm into commercialization - Take a big part in the mission to create earth's best employer - Be a respectable team leader in an open and collaborative environment

We are seeking a senior scientist with demonstrated experience in A/B testing along with related experience with observational causal modeling (e.g. synthetic controls, causal matrix completion). Our team owns "causal inference as a service" for the Pricing and Promotions organization; we run A/B tests on new pricing, promotions, and pricing/promotions CX algorithms and, where experimentation is impractical, conduct observational causal studies. Key job responsibilities We are seeking a senior scientist to help envision, design, and build the next generation of pricing, promotions, and pricing/promotions CX for Amazon. On our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design and implement in production new statistical methods for measuring causal effects of an extensive array of business policies. This position is perfect for someone who has a deep and broad analytic background, is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis and have experience coding with engineers to put projects into production. We are particularly interested in candidates with research background in experimental statistics. A day in the life - Discuss with business problems with business partners, product managers, and tech leaders - Brainstorm with other scientists to design the right model for the problem at hand - Present the results and new ideas for existing or forward looking problems to leadership - Dive deep into the data - Build working prototypes of models - Work with engineers to implement prototypes in production - Analyze the results and review with partners About the team We are a team of scientists who design and implement the econometrics powering pricing, promotions, and pricing/promotions CX.

The Edge CV team under Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with computer vision and multimodal perception models for various edge applications. Key job responsibilities As an Applied Scientist with the Edge CV team under AGI, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with multimodal models with an emphasis on computer vision. Your work will directly impact our customers in the form of products and services that make use of CV technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in AGI in within perception domain. A day in the life An Applied Scientist with the AGI team will support the science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into practice. About the team The Edge CV team has a mission to deliver best in class CV and multimodal models in support of various low latency perception based applications for devices like Echo Show series within Amazon.

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLMs, etc. The ideal candidate has the ability to understand, implement, innovate and on the state-of-the-art generative AI based systems. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow, AWS Sagemaker, and SparkML. Our team is at an early stage, so you will have significant impact on our ML deliverables with little operational load from existing models/systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. Our team is leading ML and optimization features in Amazon Connect. We are a team of scientists and engineers working on multiple science projects for Amazon Connect. We use state-of-the-art science and engineering practices to address the hard problems in contact center operation and management for our customers, and we move fast to implement solutions and refine them based on customer feedback. Learn more about Amazon Connect here: https://aws.amazon.com/connect/ About the team 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 (gender diversity) 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

The Alexa Conversational Assistants Services (CAS) org is looking for a Senior Applied Scientist with a background in Natural Language Processing, Machine/Deep Learning, and Large Language Models. You will be working with a team of applied and research scientists to enhance existing features and explore new possibilities with LLM empowerment. You will own high visibility programs with broad visibility and impact. You will interact with a cross-functional team of science, product, and engineering leaders. We are looking for Senior Applied Science Manager who will play a key role in the next generation of AI powered Conversational Assistants. Key job responsibilities Lead and manage a team of applied and research scientists, and language engineers, data linguists responsible for building conversational assistants Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. Mentor and guide team members to achieve their career goals and objectives. Communicate research findings and progress to senior leadership and stakeholders.

The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications.

Amazon’s Global Media and Entertainment (GME) organization is creating a future of entertainment where creative content, innovation, and commerce come together. We leverage Amazon’s unique expertise across video, music, gaming, and more to create a truly immersive entertainment experience. Our team, GME Science, is focused on building science tools to optimize Amazon’s entertainment offerings, so that we can provide a great customer experience while operating as a sustainable and profitable business. We push ourselves to Think Big, building ambitious models that create value in multiple GME businesses. This role will expand our team’s measurement work. Business leaders need to quickly understand the long-term impact of various investments, such as new website features, content creation, or marketing campaigns. Our team figures out how to take short-term signals – such as clicks or signups – and turn them into estimates of long-term financial impacts. We work with measurement teams in each business as well as central teams to build foundational measurement science and adapt it for unique use cases. One particular application for this role is to build a principled approach to valuing content/talent deals that include multiple GME businesses. Each deal is unique, featuring talent from film, sports, music, and other top industries, with contract terms that could include video content, podcasts, live appearances, and more. Our valuations need to be structured so that they are comparable across deals, yet flexible enough to account for diverse contracts. To be successful in this role, you will need effective communication, an ability to work closely with stakeholders across our many GME partner teams, and the skill to translate data-driven findings into actionable insights. This includes developing a deep understanding of our business context, which is ambiguous and can change quickly. Your work will be used by decision-makers across GME to deliver the best entertainment experience for our customers, which means we have a high bar. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help GME leaders ask the right questions, and then deliver data-driven answers, creating the future of GME at Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across GME businesses • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews