3 questions with Antia Lamas-Linares about the Nobel Prize in physics

The head of Amazon Web Services’ quantum communication program on the Nobel winners’ influence on her field.

Last week, the Swedish Academy announced that John Clauser, Alain Aspect, and Anton Zeilinger had won the Nobel Prize in physics “for experiments with entangled photons, establishing the violation of Bell inequalities, and pioneering quantum information science”.

Antia Lamas-Linares, quantum networking lead at Amazon Web Services and an expert in quantum optics, is seen looking into the camera
Antia Lamas-Linares, a principal research scientist with Amazon Web Services (AWS) and head of the AWS Center for Quantum Networking.

When two quantum subsystems — particles, atoms, or molecules — are “entangled”, then measurements performed on one are correlated with measurements performed on the other. The experimental setup that Clauser, Aspect, and Zeilinger investigated — in which entangled quantum subsystems have been widely separated — is closely related to those used in modern quantum networks.

The first application of quantum networking is likely to be quantum key distribution, in which two parties exchange private cryptographic keys and use quantum entanglement to ensure that their communication channel hasn’t been compromised.

On the occasion of the Swedish Academy’s announcement, Amazon Science asked Antia Lamas-Linares, who leads Amazon Web Services’ Center for Quantum Networking, three questions about Clauser, Aspect, and Zeilinger’s work and its implications for her own field.

  1. Q. 

    One of the things that Clauser and Aspect did to win the Nobel Prize was a set of experiments that challenged the “hidden-variable hypothesis”. What is the hidden-variable hypothesis, and what were their experiments?

    A. 

    At the beginnings of quantum mechanics, when people started looking at this theory that was really successful at doing things like predicting the spectra coming out of atoms, and they started looking at the implications for how we understand physical theories, there was this epic battle that went on for years between Einstein and Bohr, two giants of early 20th-century physics.

    The problem was that as you look deeply into the theory, you realize that there is, for example, inherent randomness. You can prepare a couple of systems in identical ways, and you measure something about the system, and the outcomes are different. And they are different for very fundamental reasons. It's not just, oh, we just didn't have enough information. No, even with perfect information, the outcomes of identical measurement can be different. This was not compatible with how physical theories were supposed to work.

    Entanglement has made this progression from being an uncomfortable property of quantum systems — and a philosophical question — to something that constitutes the basis of quantum technologies.
    Antia Lamas-Linares

    It became a sort of hobby of Einstein’s to come up with little paradoxes based on these things that challenged the way we understand physical theories, from a very foundational point of view. In quantum mechanics, you can formally write something that we call an entangled state, and this is a state that involves more than one particle or more than one subsystem. It describes correlations between parts of that system.

    Einstein’s point was, if I take the two subsystems and separate them very far away, then I can measure something on one and know the outcome in the other one instantaneously, or faster than light communication, which is not compatible with relativity. His conclusion was that there must be an underlying theory that “explained” the quantum-mechanical correlations and the apparent randomness of measurement outcomes.

    Essentially, the results look random because there are these hidden variables that we don't know about. But if we knew their values, the results would be predictable. These theories became known as “hidden variable” theories.

    It wasn't until the ’60s that John Bell wrote a beautiful, very simple theorem where he said, let's assume that there is some underlying information, some underlying physical theory that works in this general way. Can I have a set of measurements that have different outcomes from the quantum-mechanical model?

    Related content
    New method enables entanglement between vacancy centers tuned to different wavelengths of light.

    This is called the Bell inequality. It says, if you measure a particular sequence of correlations and you get a value below a certain threshold, then there could be an underlying hidden-variable theory of quantum mechanics. But if the value is above this threshold, then quantum mechanics cannot be explained by hidden variables. At this point, no matter how uncomfortable we are with the implications of quantum mechanics, we don’t get to pretend there is an underlying theory that will explain it all away.

    It really changes our perception of the nature of reality. The randomness in measurement results is fundamental to nature. It's not just an accident from the lack of information about the hidden variables.

    Eventually, people said, Well, okay, let's test these things. For that, you need to make entangled particles, and you need to measure them in the way prescribed by Bell’s theorem. That's essentially what John Clauser and Alain Aspect did during the late ’70s and early ’80s, with increasing levels of sophistication. Anton Zeilinger wasn't part of those Bell inequality measurements, but he used entanglement in a multitude of ground-breaking experiments, such as quantum teleportation, entanglement swapping, and the generation of tripartite entangled states.

    Clauser experiment
    In his original experiment, John Clauser used irradiated calcium atoms to emit pairs of entangled photons and polarizing filters to measure correlations between their polarizations.
    Adapted from a figure by Johan Jarnestad for the Royal Swedish Academy of Sciences

  2. Q. 

    So what does this all have to do with quantum networking?

    A. 

    With quantum networking, the two sides start by sharing entangled particles. The idea is that the outcomes on both sides are perfectly correlated, but if a malicious party tries to measure in-between, the correlations are broken, and this is detectable by the legitimate parties. It's related to the fact that quantum mechanics has properties like the no-cloning theorem that ensure that unknown quantum states cannot be perfectly copied. So you cannot just take any particle that is coming by and make a copy, because this inevitably alters the original state.

    Related content
    Collaboration will seek to advance the development of a quantum internet.

    So the two sides take a subset of the results, and using classical communication, they say, Hey, let's check that nobody has interfered with our measurements. What did you get for the first one? How about number 3,047? And they can check that the correlations are as expected from quantum theory. They sacrifice maybe 10% of their data to check this, and if the correlations hold, they can be sure that nobody has otherwise tampered with the entangled particles.

    Now to be clear, they're not sharing a message. They're sharing correlated random numbers, obtained from measurements on the entangled pair of particles. Correlated random numbers are exactly what you need for a symmetric cryptographic key. When you use a key as part of a cryptographic system, you need two parties to share a secret. It doesn't matter what the secret is, as long as it is not predictable. In this way quantum mechanics gives us the two requirements for a cryptographic key: randomness and privacy.

  3. Q. 

    It’s been 50 years since Clauser’s original experiment. How has our thinking about these questions changed in the interim?

    A. 

    What’s exciting about this is that entanglement has made this progression from being an uncomfortable property of quantum systems — and a philosophical question — to something that constitutes the basis of quantum technologies. We no longer consider entanglement an uncomfortable consequence of quantum mechanics. It is now a resource.

    Related content
    Among the ‘first wave’ of scientists to gain a PhD in quantum technology, the senior manager of research science discusses her two-decade-long career journey.

    Alain Aspect has said that when he was doing his PhD, he was repeatedly discouraged from pursuing these experiments because working on the foundations of physics was a career killer. This is not what you do if you want to be successful.

    My own PhD centered on building entanglement sources, which is a very engineering-focused task. I needed to engineer a system — in my case, a nonlinear crystal — to give me higher-quality entangled photon pairs. I wasn’t trying to prove philosophical questions regarding entanglement.

    You could consider large parts of what Oskar [Painter, head of quantum hardware at Amazon Web Services] does and what I do as “entanglement engineering”. Our groups in AWS do quantum engineering, and entanglement is a vital resource that we produce, channel, and measure. That is a pretty radical evolution from where things stood when John, Alain, and Anton did their groundbreaking work.

Research areas

Related content

US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Quantum Research Scientist in the Processor Test and Measurement group. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a deep and broad knowledge of experimental measurement techniques. Candidates with a track record of original scientific contributions will be preferred. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As a research scientist you will be expected to work on new ideas and stay abreast of the field of experimental quantum computation. Key job responsibilities We are looking to hire a Research Scientist to develop and test novel calibration and optimization tools for Quantum Error Correction on large scale quantum processors. You will be on a team of engineers and scientists at the frontier of quantum processor control and error correction. You are expected to take part in high-impact research projects that intersect with our engineering roadmap. We are looking for candidates with strong engineering principles and resourcefulness. Organization and communication skills are essential. A day in the life 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. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. 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. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
US, WA, Seattle
Amazon's Worldwide Pricing & Promotions organization is seeking a strong Applied Scientist to help solve complex business problems involving promotional strategies at a global scale. This Applied Scientist will operate in a team of other scientists and economists. Our team applies causal inference, statistics, machine learning, forecasting, optimization, economics, and experimentation to drive actionable insights and to improve strategic business decision-making. This is an individual contributor role that requires collaboration across teams and functions to solve core business problems for the company around setting promotional strategies. The work is part of significant scientific investments in promotions intelligence systems that forecast customer demand and optimize promotions strategies across different surfaces. Key job responsibilities * Invent or adapt new scientific approaches, models, or algorithms inspired and driven by customers' needs and benefits * Produce research papers and reports that have the same level of correctness, scholarship, usefulness, completeness, depth, rigor, and originality as a top-tier external publication * Implement solutions that will be deployed into production or directly support production systems * Write clear, useful documentation describing algorithms and design choices in your components to make it possible for others to understand and reproduce your work * Contribute to operational excellence in the team's deliverable * Analyze the performance of your methods and models to understand the gaps, and iteratively propose solutions to improve * Champion the adoption of scientific advancements in the team * Help new teammates ramp up and understand who our customers are, what their needs are, how the team's solutions work, and how scientific components fit in those solutions A day in the life As an Applied Scientist on the WW Promotions Science team, you invent or adapt new scientific approaches, models, or algorithms to solve real-world business problems. Your work uses the latest (or the most appropriate) techniques from academic literature. You work semi-autonomously to successfully deliver solutions that are consistently of high quality (efficient, reproducible, testable code). You work collaboratively with teammates, partners, and stakeholders. You recognize discordant views and take part in constructive dialogue to resolve them. You adopt and identify opportunities to refine mechanisms to raise the general scientific knowledge in the team. About the team The WW Promotions Science team is responsible for driving scientific innovation to support pricing and promotions programs across Amazon's businesses. We specialize in experimental and observational causal methods, forecasting, and optimization. We apply these tools to drive business decision making at scale, leading to launch decisions of new pricing algorithms and new promotion strategies, understanding short- and long-term value of different programs, and the prioritization of budget allocations. We also develop models to set optimal prices and promotions, and define innovative price guardrails and incentives to optimize for long-term program health.
US, MA, North Reading
About the Role Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges. We're seeking a Senior Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale. What You'll Do As a Senior Applied Scientist, you'll develop and deploy machine learning models that enable mobile manipulators to perform complex tasks autonomously. You'll work at the intersection of perception, learning, and control to create intelligent systems that can adapt to diverse warehouse scenarios with minimal task-specific programming. Key job responsibilities • Design, develop, train, and deploy deep learning models for perception tasks (e.g., object detection, segmentation, pose estimation, tracking) • Develop and maintain robust camera calibration pipelines (intrinsic, extrinsic, hand-eye calibration, multi-camera systems) • Build perception systems for robotic manipulation including grasp detection, object pose estimation, and visual servoing • Improve model performance through architecture optimization, data curation, and training strategies • Build and maintain production-quality perception codebases with proper testing and documentation • Profile and optimize models for real-time inference on embedded/edge platforms • Collaborate with cross-functional teams (robotics, motion planning, controls) to integrate perception outputs for manipulation tasks • Establish best practices for model versioning, experiment tracking, and MLOps • Mentor junior engineers and contribute to technical roadmap planning A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun
US, WA, Seattle
Advertising is a complex, multi-sided market with many technologies at play within the industry. The industry is rapidly growing and evolving as viewers are shifting from traditional TV viewing to streaming video and publishers are increasingly adding video content to their online experiences. Amazon’s video advertising is a rising competitor in this industry. Amazon’s service has differentiated assets in our customer & audience insights, exclusive video content, and associated inventory that position us well as an end-to-end service for advertisers and agencies. We are innovating at the intersection of advertising, e-commerce, and entertainment. Amazon Publisher Monetization (APM) is looking for a a passionate and experienced scientist who is adept at a variety of skills; especially in generative AI, computer vision, and large language models that will accelerate our plans to maximize yield via AI-driven contextual targeting, Ads syndication and more. The ideal candidate will be an inventor at heart, they will provide science expertise, rapidly prototype, iterate, and launch, foster the spirit of collaboration and innovation within our larger sister teams and their scientists, and execute against a compelling product roadmap designed to bring AI-led science innovation to solve one of the most challenging problems in advertising. Key job responsibilities This role is focused on shaping our approach to the solving the trifecta of advertising - serving the right ad to the right viewer at the right moment - delivering engaging ads for viewers, improved performance for advertisers, and maximizing the yield of our supply inventory. Responsibilities include: * Partner deeply with Product and Engineering to develop AI-based solutions to generating contextual signals across both video (VOD and Live) and display ads. * Drive end-to-end applied science projects that have a high degree of ambiguity, scale, complexity. * Provide technical/science leadership related to computer vision, large language models and contextual targeting. * Research new and innovative machine learning approaches. * Partner with Applied Scientists across the broader org to make the most of prior art and contribute back to this community the innovation that you come up with.
IN, KA, Bengaluru
Alexa International is looking for passionate, talented, and inventive Senior Applied Scientists to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. Senior applied scientists will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services. Key job responsibilities As a Applied Scientist with II the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer’s location and language. * Quickly experiment and set up experimentation framework for agile model and data analysis or A/B testing. * Contribute through industry-first research to drive innovation forward. * Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems. * Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams. * Serve as a scientific thought leader, communicating solutions clearly to partners, stakeholders, and senior leadership. * Actively mentor junior scientists and contribute to the broader internal and external scientific community through publications and community engagement.
US, NY, New York
About the Role In this role, you will own the science strategy and technical vision for this intelligence layer, leading a team of applied scientists working across GenAI and predictive modeling. You will shape how heterogeneous signals — text, behavioral, network, temporal — come together to power talent applications at Amazon scale, from workforce forecasting to personalized development to compensation strategy. You will identify opportunities where science investment can have material impact on long-term objectives or annual goals and build consensus around needed investments, working comfortably across different modeling paradigms and data modalities to guide principal and senior scientists in their most challenging and strategic decisions while serving as the strategic science advisor to PXT leaders operating at the Director, VP, and SVP levels. As a hands-on leader, you will personally own development and delivery of the most complex science problems at the intersection of multiple ML disciplines, stay current with emergent AI/ML science and engineering trends to influence focus areas in a rapidly evolving landscape, and participate in organizational planning, hiring, mentorship, and leadership development. Key job responsibilities • Lead technical initiatives in people science models, driving breakthrough approaches through hands-on research and development in areas like foundation models for predictive modeling, efficient multi-modal LLMs, and zero-shot learning • Design and implement novel ML architectures that push the boundaries of how workforce signals are represented, fused, and predicted at scale • Guide technical direction for research initiatives across the team, ensuring robust performance in production environments serving hundreds of thousands of employees • Mentor and develop senior scientists while maintaining strong individual technical contributions on the most complex cross-domain problems • Collaborate with engineering teams to optimize and scale models for real-world talent applications • Influence technical decisions and implementation strategies across teams, shaping the long-term platform architecture About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work. As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data.
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
IN, KA, Bengaluru
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences
GB, London
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 200 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. The Insights team is looking for an Applied Scientist for our London office experienced in generative AI and large models. This is a wide impact role working with development teams across the UK, India, and the US. This greenfield project will deliver features that reduce the operational load for internal Prime Video builders and for this, you will need to develop personalized recommendations for their services. You will have strong technical ability, excellent teamwork and communication skills, and a strong motivation to deliver customer value from your research. Our position offers opportunities to grow your technical and non-technical skills and make a global impact immediately. 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 Prime Video wide processes - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports A day in the life You will lead the design of machine learning models that scale to very large quantities of data across multiple dimensions. You will embody scientific rigor, designing and executing experiments to demonstrate the technical effectiveness and business value of your methods. You will work alongside other scientists and engineering teams to deliver your research into production systems. About the team Our team owns Prime Video observability features for development teams. We consume PBs of data daily which feed into multiple observability features focussed on reducing the customer impact time.
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 an 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.