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A grid shows images from the top Amazon Science articles of 2021, the year 2021 can be seen in an overlay
These are images from some of the most engaging stories published on Amazon Science in 2021.

The top Amazon Science articles of 2021

From quantum chess to robot arms to body fat percentage and ML-powered grocery shopping, these 10 articles resonated with readers in 2021.

By Staff writer
December 28, 2021
2 min read
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  1. An Amazon quantum computing scientist wins the first-ever quantum chess tournament
    A picture of Aleksander Kubica
    Aleksander Kubica, research scientist with the AWS quantum computing team
    Credit: Perimeter Institute for Theoretical Physics

    Not only did Aleksander Kubica, a research scientist with the AWS quantum computing team, win the first-ever tournament, he did so having played quantum chess only once — the weekend before the event. Learn about his strategy and watch video from two of his historic matches.

  2. A conversation with Michael I. Jordan, Michael Kearns, and Bernhard Schölkopf
    Larry Hardesty, the Amazon Science blog editor, interviewed Amazon distinguished scientist Bernhard Schölkopf (top right) and Amazon Scholars Michael Kearns (bottom left) and Michael I. Jordan (bottom right).
    Larry Hardesty, the Amazon Science blog editor, interviewed Amazon distinguished scientist Bernhard Schölkopf (top right) and Amazon Scholars Michael Kearns (bottom left) and Michael I. Jordan (bottom right).

    On the cusp of the 2021 NeurIPS conference, the three Amazon-affiliated researchers — all of whom have given the conference’s major named lecture, the Posner lecture — talked about the accelerating interesting in machine learning, its implications for both tech and AI research, and the path forward for AI.

  3. Graceful AI
    Stefano 2.jpg
    Stefano Soatto, vice president of applied science for AWS AI.
    Credit: Todd Cheney

    Stefano Soatto, vice president of applied science for AWS AI, writes, "As machine-learning-based decision systems improve rapidly, we are discovering that it is no longer enough for them to perform well on their own. They should also behave nicely toward their predecessors. When we replace an old trained classifier with a new one, we should expect a smooth transition and a peaceful transfer of decision powers."

  4. A look at the science that powers Amazon's advanced robot arms
    A close up of the Robin robotic arm
    Robin must calculate how to identify, move, and sort parcels that may rest atop one another as they are presented via a conveyor.
    Credit F4D Studio

    Robin, one of the most complex stationary robot arm systems Amazon has ever built, brings many core technologies to new levels and acts as a glimpse into the possibilities of combining vision, package manipulation and machine learning. Learn what separates them from more traditional robot arms — and watch them in action.

  5. Why now is a 'particularly good time' to explore careers in robotics
    A split screen screengrab from a video interview shows Siddhartha Srinivasa, left, director of Amazon Robotics AI, and Nia Jetter, Amazon Robotics AI senior principal technologist, right
    Siddhartha Srinivasa, left, director of Amazon Robotics AI, and Nia Jetter, Amazon Robotics AI senior principal technologist

    In a video interview, Siddhartha Srinivasa, director of Amazon Robotics AI, and Nia Jetter, Amazon Robotics AI senior principal technologist, discuss inspiration, their roles at Amazon, and tips for pursuing a robotics career. Watch their informative and illuminating discussion about careers in robotics.

  6. A computer vision system that can accurately predict body fat percentage
    Halo App Body Feature2.png
    With Amazon Halo's Body feature, individuals can measure their own body fat percentage and track it through a personalized 3D model.

    A team of Amazon scientists discusses the challenges in developing a system that can accurately estimate body fat percentage and create personalized 3D avatars of users from smartphone photos. Learn how they utilized convolutional neural networks, and semi-supervised learning.

  7. ARA recipient aims to unite the deep learning community
    Michael Bronstein, the chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London
    Michael Bronstein, an Amazon Research Awards (ARA) recipient, is also the chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London
    Dino Dimopoulos

    Michael Bronstein, the chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London, is using pioneering machine learning to push the boundaries of drug design, among other things — including unifing the machine learning “zoo”.

  8. The science of operations planning under uncertainty
    An Amazon employee is seen making a delivery while an electric delivery van is parked behind him on a residential street in Los Angeles
    An Amazon employee is seen making a delivery while an electric delivery van is parked behind him on a residential street in Los Angeles
    About Amazon

    When Amazon announced it would purchase 100,000 custom electric delivery vehicles as part of The Climate Pledge, a team of scientists within the Amazon Logistics (AMZL) Research organization took on the challenge of determining the best strategy for deploying them. Based on sophisticated models that simulate Amazon’s shipments and external parameters like power availability in each city, the team is developing a plan to gradually electrify Amazon’s entire fleet.

  9. How machine learning is helping Amazon improve the grocery shopping experience
    Amazon Fresh Prime bag
    From forecasting to selection, Amazon Fresh scientists are developing machine learning models that seek to enhance the grocery shopping experience.

    From forecasting to selection, Amazon Fresh scientists are working through (often open) research challenges to develop machine learning models that seek to enhance the grocery shopping experience. Learn how Amazon Fresh is leveraging scientific innovation to meet increased consumer demand.

  10. Creating sustainable, data-driven buildings
    Sense, Act, and Scale 2
    The path to improving building energy efficiency can be paved with the framework of sense, act, and scale say Bharathan Balaji and Rob Aldrich

    Bharathan Balaji , an Amazon senior research scientist, and Rob Aldrich, senior sustainability strategist, write about what must happen to meet the United Nations Global Status Report goal of making buildings at least 30% more energy efficient in order to achieve Paris Agreement goals.

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Structural Design Analysis Engineer, Structural Design Integrity
US, CA, Sunnyvale
Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced industry leading devices like Fire tablets, Fire TV and Amazon Echo. As a Design Analysis Engineer, you will be responsible for bringing new product designs through to manufacturing. Structural engineering contributes unique, in-depth technical knowledge to solve complex engineering problems in concert with multi-disciplinary teams including Industrial Design, Hardware Engineering, and Operations. Key job responsibilities You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will: · Perform analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Develop, analyze and test thermal, acoustic and structural solutions; from concept design, feature development, product architecture, through system validation · Support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Use simulation tools like Abaqus for analysis and design of products · Validate design modifications using simulation and actual prototypes · Use of programming languages like Python and Matlab for analytical/statistical analyses and automation · Establish noise thresholds for usability and compliance requirements · Determine and validate structural performance under use and test conditions · Have strong knowledge of various materials such as heat spreaders solutions to resolve thermal issues, damping materials for noise and vibration suppression · Use various data acquisition systems with thermocouples, accelerometers, strain gauges and IR cameras · Collaborate as part of the device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives · Design and execute tests using statistical tools to validate analytical models, identify risks and assess design margins · Create and present analytical and experimental results · Develop and apply design guidelines based on project results
Sr. Applied Scientist, Workforce Solutions
CA, BC, Vancouver
Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure. We are looking for a Sr. Applied Scientist to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Software Engineers, Machine Learning Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities * Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams * Priority management - manage diverse requests and dependencies from teams * Process improvements – define, implement and continuously improve delivery and operational efficiency * Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations * Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better.
Research Scientist, Fauna
US, NY, New York
We are seeking an Research Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
Applied Science Manager, Prime Video Personalization & Discovery
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Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.
Applied Scientist II, Prime AI/ML Science
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, 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 optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. 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, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science 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 AI/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. Key job responsibilities - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science 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 AI/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.
Applied Scientist, Amazon Leo Satellite Build Systems
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Build the scientific intelligence layer powering Amazon’s satellite manufacturing system. As an Applied Scientist, you will develop machine learning models that transform fragmented manufacturing, test, quality, and operational data into actionable intelligence that improves how satellites are built. You will tackle ambiguous, high-impact problems where data is incomplete, noisy, and distributed, and where model outputs influence real-world manufacturing decisions. Your work will power AI-enabled workflows such as non-conformance disposition, root-cause analysis, and predictive test optimization - reducing defects, accelerating production, and helping create more intelligent, data-driven manufacturing systems. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be 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. Key job responsibilities - Translate ambiguous manufacturing and operational problems into well-defined scientific problems, modeling approaches, and evaluation criteria - Design, train, and deploy machine learning models, including LLM-based systems, retrieval models, and task-specific models - Develop and evaluate models using large-scale, noisy, heterogeneous datasets with incomplete, delayed, or imperfect ground truth - Apply state-of-the-art techniques in areas such as anomaly detection, root-cause inference, multimodal learning, information retrieval, and generative AI, adapting or extending them to meet project requirements - Design experiments and evaluation frameworks that capture real-world failure modes, distribution shift, and decision risk - Make principled tradeoffs among model complexity, data quality, accuracy, latency, cost, and maintainability - Build production-quality scientific components with appropriate testing, documentation, monitoring, and operational mechanisms - Work with Manufacturing, Quality, Test, and engineering partners to understand customer needs and translate them into effective scientific solutions - Analyze model and system performance, identify gaps and root causes, and iteratively improve deployed solutions - Clearly document scientific approaches, experimental results, design decisions, and lessons learned so that others can understand and reproduce the work - Contribute to technical discussions, mentor less experienced teammates, and help advance scientific and engineering best practices within the team A day in the life You may start by partnering with Quality and Manufacturing teams to define a training dataset for a root-cause prediction model, including how historical cases should be labeled and evaluated. You then design experiments and train models, comparing approaches across architectures, features, and data slices. Later, you analyze benchmark results to identify failure modes, data-quality issues, and generalization gaps, and refine the evaluation set to better represent real-world cases. You work with engineers to integrate the model into a production workflow, adding testing, monitoring, and feedback mechanisms. Throughout the day, you balance scientific rigor with practical constraints such as data availability, latency, reliability, and operational cost. About the team Leo Satellite Build Systems is the centralized AI team within Leo Production Operations. We build shared capabilities for AI across Production Operations, including governed data assets, machine learning models, retrieval systems, evaluation frameworks, and knowledge services. We work on real-world systems where scientific decisions can influence physical outcomes. We value rigorous experimentation, strong data foundations, clear documentation, and production-ready engineering. Our team is helping enable AI-native manufacturing by turning fragmented operational knowledge and data into reliable intelligence that improves production outcomes.
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You will be working with a unique and gifted team developing exciting products for consumers. The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. The team faces a challenging task of balancing cost, schedule, and performance requirements. You should be comfortable collaborating in a fast-paced and often uncertain environment, and contributing to innovative solutions, while demonstrating leadership, technical competence, and meticulousness. Your deliverables will include development of thermal solutions, concept design, feature development, product architecture and system validation through to manufacturing release. You will support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques. Key job responsibilities * Evaluate and optimize thermal solution requirements of consumer electronic products * Use simulation tools like Star-CCM+ or FloTherm XT/EFD for analysis and design of products * Validate design modifications for thermal concerns using simulation and actual prototypes * Establish temperature thresholds for user comfort level and component level considering reliability requirements * Have intimate knowledge of various materials and heat spreaders solutions to resolve thermal issues * Use of programming languages like Python and Matlab for analytical/statistical analyses and automation * Collaborate as part of device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives * Design and execute of tests using statistical tools to validate analytical models, identify risks and assess design margins * Create and present analytical and experimental results * Develop and apply design guidelines based on project learnings
Applied Science Manager, Advertising Trust
IN, KA, Bengaluru
Amazon Ads delivers advertising experiences across Amazon's owned-and-operated properties and third-party networks, reaching hundreds of millions of customers worldwide. Within Amazon Ads, Advertising Trust is the science-first organization responsible for ensuring every ad shown to customers meets Amazon's content policies — at massive scale, across all ad formats and global marketplaces. The Ads Trust Science team builds the ML systems that automate content moderation decisions: multimodal classification, retrieval-based labeling, LLM reasoning, and agentic self-improvement architectures. This requires inventing new approaches at the intersection of computer vision, NLP, information retrieval, and generative AI. We are seeking an Applied Science Manager to lead a team of applied scientists building next-generation content moderation intelligence. You will own the science roadmap for one of the highest-impact automation programs in Amazon Advertising, defining how multimodal content understanding, retrieval-first classification, and LLM-based reasoning combine into a production system that serves global advertising at scale. Key job responsibilities * Lead a team of applied scientists working across multimodal ML (vision-language models, video understanding), large-scale retrieval systems (embedding-based similarity and deduplication), and generative AI (LLM-based policy reasoning, knowledge distillation, agentic architectures, reinforcement learning). * Define the science strategy for ads trust. * Own end-to-end delivery of ML solutions: problem formulation, offline experimentation, online A/B testing, and production deployment. Your models directly move automation and defect metrics reported to senior leadership. * Build and grow scientists — hire, mentor, and develop team members. Raise the science bar through structured review processes and a publication culture within Amazon. * Partner with engineering, product, and operations teams to translate science investments into measurable automation improvements. Influence roadmaps across dependent teams. * Communicate science strategy and results to senior leadership through narratives, technical deep-dives, and roadmap documents.
Applied Scientist II, Amazon Search
IN, KA, Bengaluru
We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke. As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Results Quality and Search Autocomplete. We are looking for a Applied Scientist II to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. You will build systems that anticipate search query intent and surface the right results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality search results. You will: - Develop and deploy ML models to produce relevant search results. - Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for ranking and relevance. - Develop reinforcement learning and reward-modeling approaches to continuously improve search results quality. - Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance. - Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment. - Lead end-to-end science projects from problem formulation through production launch, collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.
Applied Scientist II, Amazon Search
IN, KA, Bengaluru
We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke. As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Results Quality and Search Autocomplete. We are looking for a Applied Scientist II to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. You will build systems that anticipate search query intent and surface the right results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality search results. You will: - Develop and deploy ML models to produce relevant search results. - Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for ranking and relevance. - Develop reinforcement learning and reward-modeling approaches to continuously improve search results quality. - Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance. - Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment. - Lead end-to-end science projects from problem formulation through production launch, collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.
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