Amazon Scholar John Preskill on the AWS quantum computing effort

The noted physicist answers 3 questions about the challenges of quantum computing and why he’s excited to be part of a technology development project.

In June, Amazon Web Services (AWS) announced that John Preskill, the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology, an advisor to the National Quantum Initiative, and one of the most respected researchers in the field of quantum information science, would be joining Amazon’s quantum computing research effort as an Amazon Scholar.

Quantum computing is an emerging technology with the potential to deliver large speedups — even exponential speedups — over classical computing on some computational problems.

John Preskill
John Preskill, the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology and an Amazon Scholar
Credit: Caltech / Lance Hayashida

Where a bit in an ordinary computer can take on the values 0 or 1, a quantum bit, or qubit, can take on the values 0, 1, or, in a state known as superposition, a combination of the two. Quantum computing depends on preserving both superposition and entanglement, a fragile condition in which the qubits’ quantum states are dependent on each other.

The goal of the AWS Center for Quantum Computing, on the Caltech campus, is to develop and build quantum computing technologies and deliver them onto the AWS cloud. At the center, Preskill will be joining his Caltech colleagues Oskar Painter and Fernando Brandao, the heads of AWS’s Quantum Hardware and Quantum Algorithms programs, respectively, and Gil Refael, the Taylor W. Lawrence Professor of Theoretical Physics at Caltech and, like Preskill, an Amazon Scholar.

Other Amazon Scholars contributing to the AWS quantum computing effort are Amir Safavi-Naeini, an assistant professor of applied physics at Stanford University, and Liang Jiang, a professor of molecular engineering at the University of Chicago.

Amazon Science asked Preskill three questions about the challenges of quantum computing and why he’s excited about AWS’s approach to meeting them.

Q: Why is quantum computing so hard?

What makes it so hard is we want our hardware to simultaneously satisfy a set of criteria that are nearly incompatible.

On the one hand, we need to keep the qubits almost perfectly isolated from the outside world. But not really, because we want to control the computation. Eventually, we’ve got to measure the qubits, and we've got to be able to tell them what to do. We're going have to have some control circuitry that determines what actual algorithm we’re running.

So why is it so important to keep them isolated from the outside world? It's because a very fundamental difference between quantum information and ordinary information expressed in bits is that you can't observe a quantum state without disturbing it. This is a manifestation of the uncertainty principle of quantum mechanics. Whenever you acquire information about a quantum state, there's some unavoidable, uncontrollable disturbance of the state.

So in the computation, we don't want to look at the state until the very end, when we're going to read it out. But even if we're not looking at it ourselves, the environment is looking at it. If the environment is interacting with the quantum system that encodes the information that we're processing, then there's some leakage of information to the outside, and that means some disturbance of the quantum state that we're trying to process.

Explore our new quantum technologies research section

Quantum computing has the potential to solve computational problems that are beyond the reach of today's classical computers. Find the latest quantum news, research papers, and more.

So really, we need to keep the quantum computer almost perfectly isolated from the outside world, or else it's going to fail. It's going to have errors. And that sounds ridiculously hard, because hardware is never going to be perfect. And that's where the idea of quantum error correction comes to the rescue.

The essence of the idea is that if you want to protect the quantum information, you have to store it in a very nonlocal way by means of what we call entanglement. Which is, of course, the origin of the quantum computer’s magic to begin with. A highly entangled state has the property that when you have the state shared among many parts of a system, you can look at the parts one at a time, and that doesn't reveal any of the information that is carried by the system, because it's really stored in these unusual nonlocal quantum correlations among the parts. And the environment interacts with the parts kind of locally, one at a time.

If we store the information in the form of this highly entangled state, the environment doesn't find out what the state is. And that's why we're able to protect it. And we've also figured out how to process information that's encoded in this very entangled, nonlocal way. That's how the idea of quantum error correction works. What makes it expensive is in order to get very good protection, we have to have the information shared among many qubits.

Q: Today’s error correction schemes can call for sharing the information of just one logical qubit — the one qubit actually involved in the quantum computation — across thousands of additional qubits. That sounds incredibly daunting, if your goal is to perform computations that involve dozens of logical qubits.

Well, that's why, as much as we can, we would like to incorporate the error resistance into the hardware itself rather than the software. The way we usually think about quantum error correction is we’ve got these noisy qubits — it's not to disparage them or anything: they're the best qubits we've got in a particular platform. But they're not really good enough for scaling up to solving really hard problems. So the solution which at least theoretically we know should work is that we use a code. That is, the information that we want to protect is encoded in the collective state of many qubits instead of just the individual qubits.

We're interested in what is fundamentally different between classical systems and quantum systems. And I don't know a statement that more dramatically expresses the difference than saying that there are problems that are easy quantumly and hard classically.

But the alternative approach is to try to use error correction ideas in the design of the hardware itself. Can we use an encoding that has some kind of intrinsic noise resistance at the physical level?

The original idea for doing this came from one of my Caltech colleagues, Alexei Kitaev, and his idea was that you could just design a material that sort of has its own strong quantum entanglement. Now people call these topological materials; what's important about them is they're highly entangled. And so the information is spread out in this very nonlocal way, which makes it hard to read the information locally.

Making a topological material is something people are trying to do. I think the idea is still brilliant, and maybe in the end it will be a game-changing idea. But so far it's just been too hard to make the materials that have the right properties.

A better bet for now might be to do something in-between. We want to have some protection at the hardware level, but not go as far as these topological materials. But if we can just make the error rate of the physical qubits lower, then we won't need so much overhead from the software protection on top.

Q: For a theorist like you, what’s the appeal of working on a project whose goal is to develop new technologies?

My training was in particle physics and cosmology, but in the mid-nineties, I got really excited because I heard about the possibility that if you could build a quantum computer, you could factor large numbers. As physicists, of course, we're interested in what is fundamentally different between classical systems and quantum systems. And I don't know a statement that more dramatically expresses the difference than saying that there are problems that are easy quantumly and hard classically.

The situation is we don't know much about what happens when a quantum system is very profoundly entangled, and the reason we don't know is because we can't simulate it on our computers. Our classical computers just can't do it. And that means that as theorists, we don't really have the tools to explain how those systems behave.

I have done a lot of work on these quantum error correcting codes. It was one of my main focuses for almost 15 years. There were a lot of issues of principle that I thought were important to address. Things like, What do you really need to know about noise for these things to work? This is still an important question, because we had to make some assumptions about the noise and the hardware to make progress.

I said the environment looks at the system locally, sort of one part at a time. That's actually an assumption. It's up to the environment to figure out how it wants to look at it. As physicists, we tend to think physics is kind of local, and things interact with other nearby things. But until we’re actually doing it in the lab, we won't really be sure how good that assumption is.

So this is the new frontier of the physical sciences, exploring these more and more complex systems of many particles interacting quantum mechanically, becoming highly entangled. Sometimes I call it the entanglement frontier. And I'm excited about what we can learn about physics by exploring that. I really think in AWS we are looking ahead to the big challenges. I'm pretty jazzed about this.

#403: Amazon Scholars

On November 2, 2020, John Preskill joined Simone Severini, the director of AWS Quantum Computing, for an interview with Simon Elisha, host of the Official AWS Podcast.

Research areas

Related content

IN, KA, Bengaluru
Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalised, and effective experience. Alexa Sensitive Content Intelligence (ASCI) team is developing responsible AI (RAI) solutions for Alexa+, empowering it to provide useful information responsibly. The Mission Build AI safety systems that protect millions of Alexa customers every day. As conversational AI evolves, you'll solve challenging problems in Responsible AI by ensuring LLMs provide safe, trustworthy responses, building AI systems that understand nuanced human values across cultures, and maintaining customer trust at scale. We are looking for a passionate, talented, and inventive Data Scientist-II to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring good learning and generative models knowledge. You will be working with a team of exceptional Data Scientists working in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with other data scientists while understanding the role data plays in developing data sets and exemplars that meet customer needs. You will analyze and automate processes for collecting and annotating LLM inputs and outputs to assess data quality and measurement. You will apply state-of-the-art Generative AI techniques to analyze how well our data represents human language and run experiments to gauge downstream interactions. You will work collaboratively with other data scientists and applied scientists to design and implement principled strategies for data optimization. Key job responsibilities A Data Scientist-II should have a reasonably good understanding of NLP models (e.g. LSTM, LLMs, other transformer based models) or CV models (e.g. CNN, AlexNet, ResNet, GANs, ViT) and know of ways to improve their performance using data. You leverage your technical expertise in improving and extending existing models. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing in your career, this may be the place for you. A day in the life You will be working with a group of talented scientists on running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation for worldwide coverage. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, model development, and solution implementation. You will work with other scientists, collaborating and contributing to extending and improving solutions for the team. About the team Our team pioneers Responsible AI for conversational assistants. We ensure Alexa delivers safe, trustworthy experiences across all devices, modalities, and languages worldwide. We work on frontier AI safety challenges—and we're looking for scientists who want to help shape the future of trustworthy AI.
IN, KA, Bengaluru
Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalised, and effective experience. Alexa Sensitive Content Intelligence (ASCI) team is developing responsible AI (RAI) solutions for Alexa+, empowering it to provide useful information responsibly. The Mission Build AI safety systems that protect millions of Alexa customers every day. As conversational AI evolves, you'll solve challenging problems in Responsible AI by ensuring LLMs provide safe, trustworthy responses, building AI systems that understand nuanced human values across cultures, and maintaining customer trust at scale. We are looking for a passionate, talented, and inventive Data Scientist-II to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring good learning and generative models knowledge. You will be working with a team of exceptional Data Scientists working in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with other data scientists while understanding the role data plays in developing data sets and exemplars that meet customer needs. You will analyze and automate processes for collecting and annotating LLM inputs and outputs to assess data quality and measurement. You will apply state-of-the-art Generative AI techniques to analyze how well our data represents human language and run experiments to gauge downstream interactions. You will work collaboratively with other data scientists and applied scientists to design and implement principled strategies for data optimization. Key job responsibilities A Data Scientist-II should have a reasonably good understanding of NLP models (e.g. LSTM, LLMs, other transformer based models) or CV models (e.g. CNN, AlexNet, ResNet, GANs, ViT) and know of ways to improve their performance using data. You leverage your technical expertise in improving and extending existing models. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing in your career, this may be the place for you. A day in the life You will be working with a group of talented scientists on running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation for worldwide coverage. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, model development, and solution implementation. You will work with other scientists, collaborating and contributing to extending and improving solutions for the team. About the team Our team pioneers Responsible AI for conversational assistants. We ensure Alexa delivers safe, trustworthy experiences across all devices, modalities, and languages worldwide. We work on frontier AI safety challenges—and we're looking for scientists who want to help shape the future of trustworthy AI.
EG, Cairo
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
US, CA, San Diego
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their macroeconomics and forecasting skillsets to solve real world problems. The intern will work in the area of forecasting, developing models to improve the success of new product launches in Private Brands. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis About the team The Amazon Private Brands Intelligence team applies Machine Learning, Statistics and Econometrics/economics to solve high-impact business problems, develop prototypes for Amazon-scale science solutions, and optimize key business functions of Amazon Private Brands and other Amazon orgs. We are an interdisciplinary team, using science and technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon, covering areas such as pricing, discovery, negotiation, forecasting, supply chain and product selection/development.
US, VA, Arlington
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team The SPB Offsite team builds solutions to extend campaigns to reach customers off the store and extend shopping experiences on third party sites where shoppers search and discover products. We use industry leading machine learning, high scale low latency systems, and AI technologies to create better sponsored customer experiences off the store. This role will have deep interest in building the next innovations in ad tech and shopping wherever shoppers go. You will work with external and internal partners to connect ad tech systems, understand customers, and drive results at scale. You are a deeply technical leader who operates with a GenAI first approach to product, engineering, and science based solutions. As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
US, MA, Boston
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON WEB SERVICES, INC. Offered Position: Data Scientist III Job Location: Boston, Massachusetts Job Number: AMZ9674163 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. Position Requirements: Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science or a related field and two years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and two years of experience. Must have one year of experience in the following skills: (1) building statistical models and machine learning models using large datasets from multiple resources; (2) building complex data analyses by leveraging scripting languages including Python, Java, or related scripting language; and (3) communicating with users, technical teams, and management to collect requirements, evaluate alternatives, and develop processes and tools to support the organization. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $161,803/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON WEB SERVICES, INC. Offered Position: Research Scientist II Job Location: Seattle, Washington Job Number: AMZ9698004 Position Responsibilities: Perform and support the main psychometric aspects of exam development and operations, including but not limited to automated test assembly, item and test analyses, optimal item bank design, job task analysis, standard setting, quality assurance, and project planning. Conduct main aspects of psychometric analysis in operational work including performing item analysis using psychometric methods, building optimal test forms and pools via optimization techniques, analyzing and monitoring item bank health, setting pass standards via standard setting studies, and supporting Job Task Analysis (JTA) to define and refresh test blueprints. Conduct main aspects of psychometric analysis in developing and applying statistical and psychometric modeling to evaluate and ensure AWS certification exams’ validity, reliability, applicability, efficiency, and accuracy. Participate in research projects to improve existing operational processes and quality using advanced techniques such as Machine Learning (ML), statistical modeling, Natural Language Processing (NLP), Generative Artificial Intelligence (GenAI), etc. Develop automation code using R or Python for psychometric workflow pipeline and other tasks to improve operational efficiencies. Present, interpret, and communicate the results of analyses to stakeholders through written and oral reports. Follow the accreditation standards set by ISO/IEC:2012 17024 and the National Council for Certifying Agencies (NCCA) as they relate to valid psychometric practices. Engage with the professional community through conferences and publications. Position Requirements: PhD or foreign equivalent degree in Statistics, Psychometrics, Educational Measurement, Quantitative Psychology, Data Science, Industrial-Organizational (I/O) Psychology, or a related field and one year of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Must have 1 year of experience in the following skill(s): 1. large-scale education, licensure, or certification assessment programs. 2. operational psychometric tasks on large-scale education, licensure, or certification assessment programs including item analysis, equating and scaling, item response theory, classical test theory, form and pool assembly, item bank health analysis, standard setting, and job task analysis. 3. at least one of the complex test designs such as linear-on-the-fly testing (LOFT), computerized adaptive testing (CAT). 4. at least one of the following areas including machine learning (ML) or natural language processing (NLP). 5. Programming skills in at least one script-based programming language (R, Python). Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $136,000/year to $184,000/ year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000
US, CA, Palo Alto
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Amazon Ads Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Machine Learning Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems. Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. We are looking for a talented Machine Learning Applied Scientist for our Amazon Ads Response Prediction team to grow the business. We are providing advanced real-time machine learning services to connect shoppers with right ads on all platforms and surfaces worldwide. Through the deep understanding of both shoppers and products, we help shoppers discover new products they love, be the most efficient way for advertisers to meet their customers, and helps Amazon continuously innovate on behalf of all customers. Key job responsibilities As a Machine Learning Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and optimization strategies to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new machine-learning modeling to optimize all aspects of Sponsored Products and Brands business About the team We are pioneers in applying advanced machine learning and generative AI algorithms in Sponsored Products and Brands business. We empower every customer with a customized discovery experiences from back-end optimization (such as customized response prediction models) to front-end CX innovation (such as widgets), to help shoppers feel understood and shop efficiently on and off Amazon.
US, WA, Seattle
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities We are looking for an Applied Science Manager to lead the Insights & Prompt Generation vertical within the Conversational Discovery Experiences (CAX) team in Sponsored Products and Brands (SPB). This team owns prompt generation, quality, personalization, and coverage for Sponsored Prompts, a new conversational ad format powered by large language models (LLMs) that helps shoppers discover products across Amazon.com. As an Applied Science Manager, you will lead a team of applied scientists and engineers to build and scale the prompt generation pipeline, develop new prompt themes and quality frameworks, and drive coverage expansion across all surfaces. You will own the science roadmap for prompt generation and personalization. You will define the metrics that measure prompt effectiveness and drive experimentation to improve CTR, helpfulness, and advertiser outcomes. This role requires strong technical depth in NLP, LLMs, and information retrieval, combined with the ability to manage and grow a science team, set research direction, and influence product strategy. You will work across organizational boundaries with engineering, product, and business teams to translate science investments into measurable business impact.
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through novel generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace ecosystem. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As a Senior Applied Scientist on our team, you will * Develop AI solutions for Sponsored Brands advertiser and shopper experiences. Build recommendation systems that leverage generative models to develop and improve campaigns. * You invent and design new solutions for scientifically-complex problem areas and/or opportunities in new business initiatives. * You drive or heavily influence the design of scientifically-complex software solutions or systems, for which you personally write significant parts of the critical scientific novelty. You take ownership of these components, providing a system-wide view and design guidance. These systems or solutions can be brand new or evolve from existing ones. * Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. * Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; * Design and conduct A/B experiments to evaluate proposed solutions based on in-depth data analyses; * Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems * Effectively communicate technical and non-technical ideas with teammates and stakeholders; * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Stay up-to-date with advancements and the latest modeling techniques in the field About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale.