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Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), Evaluation, etc. Your work will directly impact our customers in the form of novel products and services .

The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams.

Interested in helping build Prime's Machine Learning system to drive huge business impact on millions of customers? Join our team of Scientists developing algorithms to adaptively generate and experiment on new content, personalize, and optimize the Prime membership experience. This includes identifying building foundational models that serve as an abstraction of our high-dimensional customer data, understanding who our customers are, and providing them with personalized experiences. As an ML lead, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as deep learning techniques and natural language processing to abstract sequences and embeddings from customer features, offer/content features. These abstraction layers will then be used by our personalization, segmentation, and experimentation platforms. We employ techniques from deep learning, NLP, multi-armed bandits, optimization, and RL - while this role is focused on leading the cross-sectional space of deep learning, NLP, and RL. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, Andes, Bedrock ...), various ML algorithms and techniques (Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning, LLM's), and statistical modeling techniques. Major responsibilities: - Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning, NLP, and Reinforcement Learning for our Optimization Systems. - Develop offline policy estimation tools and integrate with reporting systems. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

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 preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning 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

We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.

Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for talented and experienced science leader in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP) and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have solid technical background and extensive experience in leading projects and technical teams. The ideal candidate would also have experiences in developing natural language processing systems (particularly LLM based systems) for industry applications, enjoy operating in highly dynamic and ambiguous environments, be self-motivated to take on challenging problems to deliver customer impact. Key job responsibilities As a Senior Applied Scientist, you will: * Build a strong and coherent team with particular focus on sciences and innovations in LLM technologies for conversation AI applications * Serve as a technical lead on demanding and cross-team projects, and effectively collaborating with multiple cross-organizational teams * Apply technical influence on partner teams, increasing their productivity by sharing your deep knowledge

Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Supplier Experience Science team is looking for an experienced Economist/Scientist to excel at pricing, selection, forecast and optimization. Amazon Business (AB) represents an incredible opportunity to address a vast new market segment and customer base for Amazon. We are focused on building solutions that enable B2B customers to find, research, and buy products and services across multiple devices and marketplaces. The Amazon Business Science team owns the science and analytics for key AB problems including price setting, selection additions and operations optimization. Amazon Business is a fast growing business sector. We need leaders who can think big and drive big vision into a reality. Please come to work with us if you are result driven, think big, and want to have fun and make a history. You will build the science models and the supporting structures needed to analyze, dive deep, and innovate the pricing strategies. You will also have the opportunity to present findings to cross functional team partners to drive improvements. You will work closely with other Applied/Research/Data Scientists, Economists, Data Engineers, Software Development Engineers, Program Managers and Business Partners to solve challenging problems. You need be comfortable using intellect, curiosity and technical ability to develop innovative solutions to business problems. You need learn different aspects of the business and understand how to apply science and analytics to solve high impact business problems. You will be expected to provide clear and concise explanation to results and approaches as well as provide opinion and guidance on problem solving. The ideal candidate will have outstanding leadership skills, proven ability to develop, enhance, automate, and manage science models from end to end. The ideal candidate will have strong data mining and modeling skills and will be comfortable facilitating idea creation and working from concept through to execution. The ideal candidate must have demonstrated ability to manage medium-scale automation and modeling projects, identify requirements and build methodology and tools that are mathematically grounded but also explainable operationally, apply technical skills allowing the models to adapt to changing attributes. Key job responsibilities • Contribute to supplier operations strategy development based on science models and data analysis • Develop models to measure long term impact of seller behaviors • Collaborate with product and engineering teams both within and outside of AB to launch selection and operations systems based on science and data. • Use economical techniques to create scientific solutions for business problems. • Research, experiment and implement novel approaches. • Work closely with other scientists in the team and across teams. • Work and collaborate effectively with product managers and software engineering teams to build algorithms and models and integrate successful models and algorithms in production systems. • Use the best practices in science: data integrity, design, test, and implementation and documentation. • Contribute to Amazon's Intellectual Property through patents and internal and external publications. A day in the life The economist/scientist will develop, enhance, automate, and manage science models from end to end. He/she will also have the opportunity to present findings to cross functional team partners to drive improvements. He/she will work with other Applied/Research/Data Scientists, Economists, Data Engineers, Software Development Engineers, Program Managers and Business Partners to build analytical and science models. The scientist/economist will be expected to provide clear and concise explanation to results and approaches as well as provide opinion and guidance on problem solving. About the team Amazon Business (AB) represents an incredible opportunity to address a vast new market segment and customer base for Amazon. We are focused on building solutions that enable B2B customers to find, research, and buy products and services across multiple devices and marketplaces. The Amazon Business Science team owns the science and analytics for key AB problems including price setting and selection additions.

Amazon is the 4th most popular site in the US. Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include: - Can combining supervised multi-task training with unsupervised training help us to improve model accuracy? - Can we transfer our knowledge of the customer to every language and every locale ? - Can we build foundational ML models that can serve different business lines. This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon ML. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization. Key job responsibilities Train large deep learning models with hundreds of billions parameters. Build foundational ML models that can be applied to different business applications in Amazon such as Search and Ads. Set science directions for the team, in areas such as efficient model architecture, training and data optimization/scaling, model/data/pipeline parallel techniques, and much more.

The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Scientist with fabrication and data analysis experience working on Josephson Junction elements of a superconducting circuit. The position is on-site at our lab, located on the in Pasadena, CA. The ideal candidate will have had prior experience deep diving into fabrication details and electrical test data. We are looking for candidates with strong engineering principles, resourcefulness and data science experience. Organization and communication skills are essential. Key job responsibilities * Deep dive into the physics and related data associated with Josephson Junctions or metal-insulator-metal fabrication processes. * Develop and maintain data pipeline pertinent to superconducting device fabrication, in particular Josephson Junctions or general transmon elements. * Develop analytical tools to uncover new information about established and new junction processes. * Generate both custom and standardized reports summarizing inline and end of line electrical and process data from product material runs. * Devise experiments and provide recommendations for improvement of fabrication processes. * Communicate findings with colleagues by way of crisp documentation and presentations. A day in the life The role will be vital to the fabrication team and quantum computing device integration mechanism. The candidate will provide the most current information to project leads and fabrication area owners to drive data driven decision of production runs. Once the fabrication run starts the candidate will stay close to the details of fabrication providing data analysis and quick feedback to key stakeholders. At the end of fabrication runs custom and standardized reports will be generated by the candidate to provide insights into data generated from the run. This position may require occasional weekend work. 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. About the team Our team is comprised of scientists and engineers who are building hardware that enables quantum computing technologies. Doing that requires the fabrication of quantum devices, which necessitates staying close to the details and analyzing data while building tools to better understand the data.

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. Amazon Music Search Relevance team is seeking an experienced Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval. You'll work in a collaborative environment where you can pursue applied research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web. Key job responsibilities - Use machine learning, deep learning, LLMs and NLP techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of highly innovative models for predictive learning - Work closely with software engineering teams to drive model implementations and new feature creations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches A day in the life Imagine being a part of an agile team where your ideas have the potential toreach millions. Picture working on cutting-edge consumer-facing products, where every single team member is a critical voice in the decision-making process. Envision being able to leverage the resources of a Fortune-500 company within the atmosphere of a start-up. Welcome to Amazon Music, where ideas are born and come to life as Amazon Music Unlimited, Prime Music, and so much more. About the team Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking fornew team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us aswe make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless musicexperience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.