Prime Video's work on 3-D scene reconstruction, image representation

CVPR papers examine the recovery of 3-D information from camera movement and learning general representations from weakly annotated data.

At this year’s Conference on Computer Vision and Pattern Recognition (CVPR), Prime Video is presenting a pair of papers that indicate the range of problems we work on.

In one paper, “Depth-guided sparse structure-from-motion for movies and TV shows”, we present a method for determining the camera movement and 3-D geometry of scenes depicted in videos. An important application of this work is to enable the accurate insertion of digital objects into already recorded videos. Our approach, which leverages off-the-shelf depth estimators to enhance the standard geometric-optimization approach, results in improvements of 10% to 30% on six different performance measures, relative to the best-performing prior technique.

SfM.gif
The Prime Video structure-from-motion system at work. At top is the input video. At lower left is the video with keypoints (colored circles) added. The keypoints are tracked accurately from frame to frame, and their color indicates their depth, as estimated by a machine learning model. At lower right is the 3-D model of the keypoints (whose rotation, to demonstrate the 3-D structure, is not synchronized with the video).

In the other paper, “Robust cross-modal representation learning with progressive self-distillation,” we expand on the CLIP method of using paired images and texts found online to train a model that produces image and text representations useful for downstream tasks, such as image classification or text-based image retrieval.

Where CLIP enforces a hard alignment between Web-crawled images and their associated texts, our method is more flexible, allowing for partial correspondences between a given image and texts associated with other images. We also use a self-distillation technique, in which our model progressively creates some of its own training targets, to steadily refine its representations.

Related content
Detectors for block corruption, audio artifacts, and errors in audio-video synchronization are just three of Prime Video’s quality assurance tools.

In two different image classification settings, our method outperforms CLIP across the board, by significant margins — 30% to 90% — on some datasets. Our method also consistently outperforms its CLIP counterpart on the tasks of image-based text retrieval and text-based image retrieval.

Structure-from-motion

Structure-from-motion is the problem of determining the 3-D structure of a scene from parallax — the relative displacement of objects in the scene as the camera moves. There are robust solutions for videos with large camera movements, but they don’t work as well for feature films and TV shows, where the camera movements tend to be more restrained.

The standard approach to determining structure from motion uses geometric optimization. First, the method estimates the location of a set of 3-D points in the scene, and then, based on that estimation, it re-projects them onto a 2-D image corresponding to each camera location. The optimization procedure minimizes the distance between points in the original 2-D image and the corresponding points of the 2-D projection.

We improve on this approach by introducing depth estimates performed by off-the-shelf, pretrained models. Instead of minimizing only the difference between the original and the projected 2-D points, our approach minimizes both the reprojection error of the 2-D points and the depth measurement error, relative to the output of the depth estimation model.

Double loss.png
Our approach jointly minimizes 2-D reprojection error and depth estimate error.

Our approach begins by using a standard method to detect image keypoints — salient points in the image, usually at object corners and other edge intersections — and identify their correspondences across successive frames of video. Then, through bilinear interpolation, we use the depth map obtained from an off-the-shelf depth estimator to determine the ground-truth keypoint depths. We use the depth information not only during optimization but also during the initialization stage of the process, when we produce our initial estimates of 3-D scene structure and relative camera pose.

SfM.png
The Prime Video structure-from-motion technique identifies keypoints in input video, finds their correspondences across frames, and then estimates their depth using bilinear interpolation on a dense depth map.

We experimented with several different depth estimation models and found that the results of our approach were essentially the same with all of them. And, in all cases, our approach improved substantially on the state of the art.

Cross-modal representations

In natural-language processing, the best-performing models in recent years have been built on top of language models that learn generic linguistic representations from huge corpora of unannotated public texts. The language models can then be fine-tuned for specific tasks with minimal additional data.

CLIP (contrastive language-image pretraining) seeks to do something similar for computer vision, learning generic visual representations from images harvested from the Web and their associated texts.

Related content
The switch to WebAssembly increases stability, speed.

Like many such weakly supervised models, CLIP is trained through contrastive learning. Intuitively, for each training image, the model is fed two texts: one, the positive training example, is the text associated with the image online; the other text, the negative example, is randomly chosen. CLIP learns a data representation that pulls the image and the positive text together in the representation space and pushes the image and the negative text apart.

Although CLIP has yielded impressive results on downstream computer vision tasks, its training approach has two drawbacks. First, the web-harvested data is noisy: the text associated with an image may in fact be semantically unrelated to it. Conversely, the text randomly selected as a negative example may in fact be semantically related to the image. CLIP can thus steer the model toward erroneous associations and away from correct ones.

Our method attempts to address this problem. Rather than learn a hard alignment between image and text, we learn a soft alignment, which gives the resulting model more interpretive flexibility.

For example, in one of our experiments, both the CLIP baseline and our model were trained on datasets that included images of goldfish. When presented with an image of a stained-glass window depicting a goldfish — a type of image not included in the training data — CLIP guessed that it was a guinea pig or maybe a beer glass, while our model guessed that it was a goldfish or possibly a clown fish. That is, our model learned a representation general enough to accommodate the stylization of the stained-glass artist’s rendering style.

CV model learning.png
CLIP’s contrastive-learning procedure enforces connections between web-harvested images and their associated texts (green lines, at left) while dissociating them from other images’ texts (red lines). Our approach instead privileges associated texts but also learns softer, probabilistic alignments with other images’ texts (dotted blue lines).

Our model learns its soft alignments through a self-distillation process. First, the model learns an initial data representation through the same contrastive-loss function that CLIP uses.

Over the course of training, however, we use the model itself to make predictions about the training examples and use those predictions as additional training targets. At first, the loss function gives these self-predictions little weight, but it gradually increases the weight as training progresses.

Related content
In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.

The idea is that, over time, the model learns more reliable correlations between training images and texts. Self-distillation reinforces those correlations, so the model isn’t encouraged to break semantic connections between images and texts that may very well be present in the data. Similarly, over time, the model learns to give less weight to spurious connections between images and the texts initially associated with them.

The great virtue of general representation models like ours and CLIP is that they can be applied to a wide variety of computer vision problems. So the accuracy improvements that our approach affords should pay dividends for Prime Video customers in a range of contexts over the next few years.

Research areas

Related content

US, WA, Seattle
Are you seeking an environment where you can drive innovation? WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. Our team owns sophisticated science capabilities for forecasting the WW Amazon Stores P&L, focusing on costs and the bottomline (profitability). We are looking for an outstanding Senior economist to lead new high visibility initiatives for forecasting the WW Amazon Stores P&L (focusing on costs and the bottomline). The forecasting models will be used to enable better financial planning and decision making for senior leadership up to VP level. You will build new econometric models from the ground up. The role will develop new driver based forecasting models for Retail related P&L lines that incorporate business drivers. The Sr Economist will also help generate new insights on how macroeconomic factors impact the P&L. This role will have very high visibility with senior leadership up to VP level. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial planning and decision-making through economics. The ideal candidate combines econometric acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, economists, and business leaders. You are an excellent communicator and effectively translate technical findings into business action.
US, CA, East Palo Alto
The Customer Engagement Technology team leads AI/LLM-driven customer experience transformation using task-oriented dialogue systems. We develop multi-modal, multi-turn, goal-oriented dialog systems that can handle customer issues at Amazon scale across multiple languages. These systems are designed to adapt to changing company policies and invoke correct APIs to automate solutions to customer problems. Additionally, we enhance associate productivity through response/action recommendation, summarization to capture conversation context succinctly, retrieving precise information from documents to provide useful information to the agent, and machine translation to facilitate smoother conversations when the customer and agent speak different languages. Key focus areas include: 1. Task-Oriented Dialog Systems: Building reliable, scalable, and adaptive LLM-based agents for understanding intents, determining eligibilities, making API calls, confirming outcomes, and exploring alternatives across hundreds of customer service intents, while adapting to changing policies. 2. Lifelong Learning: Researching continuous learning approaches for injecting new domain knowledge while retaining the model's foundational abilities and prevent catastrophic forgetting. 3. Agentic Systems: Developing a modular agentic framework to handle multi domain conversations through appropriate system abstractions. 4. Complex Multi-turn Instruction Following: Identifying approaches to guarantee compliance with instructions that specify standard operating procedures for handling multi-turn complex scenarios. 5. Inference-Time Adaptability: Researching inference-time scaling methods and improving in-context learning abilities of custom models to enable real-time adaptability to new features, actions, or bug fixes without solely relying on retraining. 6. Context Adherence: Exploring methods to ground responses in specific customer attributes, account information, and behavioral data to prevent hallucinations and ensure high-fidelity responses. 7. Policy Grounding: Investigating techniques to align bot behavior with evolving company policies by grounding on complex, unstructured policy documents, ensuring consistent and compliant actions. 1. End to End Dialog Policy Optimization: Researching alignment approaches to optimize successful dialog completions. 2. Scalable Evaluations: Developing automated approaches to evaluate quality of experience, and correctness of agentic resolutions Key job responsibilities 1. Research and development of LLM-based chatbots and conversational AI systems for customer service applications. 2. Design and implement state-of-the-art NLP and ML models for tasks such as language understanding, dialogue management, and response generation. 3. Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate LLM-based solutions into Amazon's customer service platforms. 4. Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of the chatbots. 5. Conduct experiments and evaluations to measure the performance of the developed models and systems, and identify areas for improvement. 6. Stay up-to-date with the latest advancements in NLP, LLMs, and conversational AI, and explore opportunities to incorporate new techniques and technologies into Amazon's customer service solutions. 7. Collaborate with internal and external research communities, participate in conferences and publications, and contribute to the advancement of the field.
IN, TN, Chennai
DESCRIPTION The Digital Acceleration (DA) team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms for solving Digital businesses problems. Key job responsibilities - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues BASIC QUALIFICATIONS - Experience building machine learning models or developing algorithms for business application - PhD, or a Master's degree and experience in CS, CE, ML or related field - Knowledge of programming languages such as C/C++, Python, Java or Perl - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. PREFERRED QUALIFICATIONS - 5+ years of building machine learning models or developing algorithms for business application experience - Have publications at top-tier peer-reviewed conferences or journals - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
CN, 11, Beijing
职位:Applied scientist 应用科学家实习生 毕业时间:2025年10月 - 2026年9月之间毕业的应届毕业生 · 入职日期:2025年6月及之前 · 实习时间:保证一周实习4-5天全职实习,至少持续5个月 · 工作地点:北京朝阳区酒仙桥路恒通商务园区 · 校招信息请参考校园招聘申请手册: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e 投递须知: 1 填写简历申请时,请把必填和非必填项都填写完整。提交简历之后就无法修改了哦! 2 学校的英文全称请准确填写。 如果您正在攻读计算机视觉、生成式AI或多模态领域的博士或硕士研究生,而且对应用科学家的实习工作感兴趣。 如果您也喜爱深入研究棘手的技术问题并提出解决方案,用成功的产品显著地改善人们的生活。 那么,我们诚挚邀请您加入亚马逊的International Technology自动化营销团队改善亚马逊节假日促销的用户体验。我们的目标是帮助亚马逊的客户找到他们所需的产品,并发现他们感兴趣的新产品。 这会是一份收获满满的工作。您每天的工作都与全球数百万亚马逊客户的体验紧密相关。您将提出和探索LLM和CV领域的创新,例如如何精准控制最前沿的基座大语言模型和图像生成模型以满足自动化的需求。您将集成这些模型到工具链中生成个性化的促销广告图,通过标注数据、建模和客户反馈来完成闭环。您对模型的选择需要能够平衡业务指标和响应时间的需求。
CN, 11, Beijing
职位:Applied scientist 应用科学家实习生 毕业时间:2025年10月 - 2026年9月之间毕业的应届毕业生 · 入职日期:2025年6月及之前 · 实习时间:保证一周实习4-5天全职实习,至少持续3个月 · 工作地点:北京朝阳区酒仙桥路恒通商务园区 · 校招信息请参考校园招聘申请手册: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e 投递须知: 1 填写简历申请时,请把必填和非必填项都填写完整。提交简历之后就无法修改了哦! 2 学校的英文全称请准确填写。 如果您正在攻读NLP,IR或搜索领域专业的博士或硕士研究生,而且对应用科学家的实习工作感兴趣。如果您也喜爱深入研究棘手的技术问题并提出解决方案,用成功的产品显著地改善人们的生活。 那么,我们诚挚邀请您加入亚马逊的International Technology搜索团队改善Amazon的产品搜索服务。我们的目标是帮助亚马逊的客户找到他们所需的产品,并发现他们感兴趣的新产品。 这会是一份收获满满的工作。您每天的工作都与全球数百万亚马逊客户的体验紧密相关。您将提出和探索NLP和IR领域的创新,基于TB级别的产品和流量数据设计机器学习模型。您将集成这些模型到搜索引擎中为客户提供服务,通过数据,建模和客户反馈来完成闭环。您对模型的选择需要能够平衡业务指标和响应时间的需求。
IL, Haifa
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help 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 from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at any time 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 240 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 We are seeking an exceptional Applied Scientist to join our Prime Video Sports tech team in Israel. Our team is dedicated to developing state-of-the-art science to allow for personalizing the customers’ experience and customers to seamlessly find any live event in our selection. You will have the opportunity to work on innovative, large-scale projects that push the boundaries of what's possible in sports content delivery and engagement. Your expertise will be crucial in tackling complex challenges such as temporal information retrieval, leveraging Generative AI and Large Language Models (LLMs), and building state-of-the-art recommender systems. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Personalization, Information Retrieval, and Recommender Systems, or general ML to lead the development of new algorithms and end-to-end solutions. As part of our team of applied scientists and software development engineers, you will be responsible for researching, designing, developing, and deploying algorithms into production pipelines. Your role will involve working with cutting-edge technologies such as Gen AI/LLMs to enhance content discovery and search capabilities. You'll also tackle unique challenges like temporal information retrieval to improve real-time sports content recommendations. As a technologist, you will drive the publication of original work in top-tier conferences in Machine Learning and Information Retrieval. We expect you to thrive in ambiguous situations, demonstrating outstanding analytical abilities and comfort in collaborating with cross-functional teams and systems. The ideal candidate is a self-starter with the ability to learn and adapt quickly in our fast-paced environment. About the team We are the Prime Video Sports team. In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
IL, Haifa
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help 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 from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at any time 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 240 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. We are seeking an exceptional Sr. Applied Scientist to join our Prime Video Sports tech team in Israel. Our team is dedicated to developing state-of-the-art science to allow for personalizing the customers’ experience and customers to seamlessly find any live event in our selection. You will have the opportunity to work on innovative, large-scale projects that push the boundaries of what's possible in sports content delivery and engagement. Your expertise will be crucial in tackling complex challenges such as temporal information retrieval, leveraging Generative AI and Large Language Models (LLMs), and building state-of-the-art recommender systems. Key job responsibilities We are looking for a Senior Applied Scientist with domain expertise in Personalization, Information Retrieval, and Recommender Systems, or general ML to lead the development of new algorithms and end-to-end solutions. As part of our team of applied scientists and software development engineers, you will be responsible for researching, designing, developing, and deploying algorithms into production pipelines. Your role will involve working with cutting-edge technologies such as GenAI/LLMs to enhance content discovery and search capabilities. You'll also tackle unique challenges like temporal information retrieval to improve real-time sports content recommendations. As a technologist, you will drive the publication of original work in top-tier conferences in Machine Learning and Information Retrieval. We expect you to thrive in ambiguous situations, demonstrating outstanding analytical abilities and comfort in collaborating with cross-functional teams and systems. The ideal candidate is a self-starter with the ability to learn and adapt quickly in our fast-paced environment. About the team We are the Prime Video Sports team. As part of this team, you will be working on the science behind the Discovery, Personalization and Search experiences of PV Sports. In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
CA, QC, Montreal
Amazon Games recherche un.e scientifique en apprentissage automatique sénior.e pour développer et intégrer de nouvelles approches d'apprentissage automatique (ML), d'apprentissage par renforcement (RL) et d'IA générative (Gen AI) dans nos processus de développement de jeux et dans nos expériences de jeux. Dans ce rôle, vous travaillerez en étroite collaboration avec nos studios de développement de jeux et nos équipes opérationnelles pour imaginer et développer des outils, des processus et des fonctionnalités alimentés par l'IA générative à travers Amazon Games. Chez Amazon Games, notre ambition est de créer de expériences inédites et audacieuses qui rassemblent et cultivent les communautés de joueurs et de joueuses. Notre équipe d'experts de l'industrie développe des jeux multijoueurs AAA et des propriétés intellectuelles originales, avec des équipes à Seattle, Orange County, San Diego, Montréal et Bucarest. À travers nos divisions - Studios, Publishing et Prime Gaming et en collaboration avec des partenaires externes, nous développons, publions et livrons des jeux et des expériences de contenu exceptionnelles pour les joueurs et joueuses. /// Amazon Games is seeking a highly effective Senior Machine Learning Scientist to build and integrate novel ML, RL and Generative AI (Gen AI) approaches into our game pipelines and customer experiences. In this role, you will work closely with our game development studios and operations teams to research and develop generative AI-powered tools, pipelines and features across Amazon Games. At Amazon Games, our ambition is to create bold new experiences that foster community in and around our games. Our team of game industry veterans develops AAA multiplayer games and original IPs, with teams in Seattle, Orange County, San Diego, Montreal, and Bucharest. Amazon Games, through its Studios, Publishing, and Prime Gaming divisions collaborating with external partners, aims to develop, publish, and deliver compelling AAA games and content experiences for gamers to discover. Key job responsibilities Responsabilités - Diriger la recherche, l'implémentation et la mise en production d'initiatives ambitieuses et complexes en IA/ML pour Amazon Games. - Collaborer avec les équipes de programmation, de conception et artistique pour concevoir, développer et intégrer de nouveaux outils d'IA générative dans les flux de travail des développeuses et développeurs. - Identifier et résoudre de manière proactive les problèmes qui affectent la qualité de vie des joueurs, des opérations et des autres développeurs. - Se tenir au courant et analyser les dernières avancées en matière de technologie d'IA générative, et améliorer continuellement les fonctionnalités des produits lorsque des améliorations significatives en termes de coût, d'évolutivité, de qualité ou de fonctionnalité peuvent être réalisées. - Consulter et contribuer aux évaluations d'autres services internes ou tiers de ML, RL et Gen AI qui pourraient être utilisés par le projet ou l'organisation. /// Responsibilities - Drive the research, implementation, and productionizing for ambitious and complex AI/ML initiatives for Amazon Games. - Collaborate with game team engineers, designers and artists to design, develop, and integrate new generative AI tools into developer workflows. - Proactively identify and solve problems that affect the quality of life for players, operations, and other developers. - Stay up to date with and analyze the latest advancements, in generative AI technology, and continuously improve product features where meaningful improvements in cost, scalability, quality, or functionality can be achieved. - Consult and contribute to evaluations of other internal or 3rd ML, RL and Gen AI services that could be leveraged by the project or the organization. A day in the life Une journée type - Vous vous épanouissez dans un environnement collaboratif où vos décisions ont un impact et une influence significatifs. - Vous exprimer votre passion par la création d'expériences de jeu qui ravissent les joueurs et les joueuses. - Vous proposez d'excellents flux de travail, outils et innovations de jeu à vos collègues et aux équipes de développement et recherchez constamment l'amélioration. - Vous souhaitez faire partie de quelque chose d'excitant et unique dans l'écosystème du jeu. /// A day in the life - You thrive in a collaborative environment where your decisions have significant impact and influence. - You are passionate about building game experiences that delight players. - You deliver great workflows, tools, and game innovations to your fellow developers and constantly seek improvement. - You want to be part of something exciting and unique in the gaming ecosystem. About the team À propos de l'équipe L'équipe de recherche en IA d'Amazon Games Studio se concentre sur l'innovation en intelligence artificielle dans le domaine du jeu vidéo. Notre équipe hautement qualifiée et multidisciplinaire travaille sur l'apprentissage automatique, l'apprentissage par renforcement et l'IA générative pour réinventer le développement des jeux. Nous travaillons de près avec les équipe internes et nos studios partenaires pour donner vie à leur vision créative. Notre mission est d'utiliser l'IA de manière responsable pour transformer l'expérience de jeu, enrichir les récits, et fournir aux créateurs et créatrices des outils pratiques pour optimiser leurs chaînes de production. /// About the Team The Amazon Games Studio AI Research team focuses on artificial intelligence innovation in gaming. Our highly skilled, multi-discipline team works across Machine Learning, Reinforcement Learning, and Generative AI to reimagine game development. We work closely with first-party game developers and partner studios to bring creative visions to life. Our mission is to use AI responsibly to transform gameplay experiences, enrich narratives, and provide creators with practical tools to optimize their production pipelines.
US, CA, San Diego
Amazon Games is seeking a highly effective Senior Machine Learning Scientist to build and integrate novel ML, RL and Generative AI (Gen AI) approaches into our game pipelines and customer experiences. In this role, you will work closely with our game development studios and operations teams to research and develop generative AI-powered tools, pipelines and features across Amazon Games. At Amazon Games, our ambition is to create bold new experiences that foster community in and around our games. Our team of game industry veterans develops AAA multiplayer games and original IPs, with teams in Seattle, Orange County, San Diego, Montreal, and Bucharest. Amazon Games, through its Studios, Publishing, and Prime Gaming divisions collaborating with external partners, aims to develop, publish, and deliver compelling AAA games and content experiences for gamers to discover. Key job responsibilities - Drive the research, implementation, and productionizing for ambitious and complex AI/ML initiatives for Amazon Games. - Collaborate with game team engineers, designers and artists to design, develop, and integrate new generative AI tools into developer workflows. - Proactively identify and solve problems that affect the quality of life for players, operations, and other developers. - Stay up to date with and analyze the latest advancements, in generative AI technology, and continuously improve product features where meaningful improvements in cost, scalability, quality, or functionality can be achieved. - Consult and contribute to evaluations of other internal or 3rd ML, RL and Gen AI services that could be leveraged by the project or the organization. A day in the life - You thrive in a collaborative environment where your decisions have significant impact and influence. - You are passionate about building game experiences that delight players. - You deliver great workflows, tools, and game innovations to your fellow developers and constantly seek improvement. - You want to be part of something exciting and unique in the gaming ecosystem. About the team The Amazon Games Studio AI Research team focuses on artificial intelligence innovation in gaming. Our highly skilled, multi-discipline team works across Machine Learning, Reinforcement Learning, and Generative AI to reimagine game development. We work closely with first-party game developers and partner studios to bring creative visions to life. Our mission is to use AI responsibly to transform gameplay experiences, enrich narratives, and provide creators with practical tools to optimize their production pipelines.
US, WA, Seattle
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!