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18,664 results found
  • Roland Maas, Ariya Rastrow, Kyle Goehner, Gautam Tiwari, Shaun Joseph, Björn Hoffmeister
    Interspeech 2017
    2017
    The task of automatically detecting the end of a device-directed user request is particularly challenging in case of switching short command and long free-form utterances. While low latency end-pointing configurations typically lead to good user experiences in the case of short requests, such as “play music”, it can be too aggressive in domains with longer free-form queries, where users tend to pause noticeably
  • Daniele Ferone, Paola Festa, Antonio Napoletano, Mauricio G. C. Resende
    LION 2017
    2017
    We propose a new smart local search for the p-center problem, based on the critical vertex concept, and embed it in a GRASP framework. Experimental results attest the robustness of the proposed search procedure and confirm that for benchmark instances it converges to optimal or near/optimal solutions faster than the best known state-of-the-art local search.
  • Carnegie Mellon University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Recent years have seen a surge in consumer usage of spoken dialog systems, due to the popularity of voice assistants. While these systems are capable of answering factual questions or executing basic tasks, they do not yet have the capability to hold multi-turn conversations. The Alexa Prize challenge provides us a great opportunity to explore various approaches and dialog strategies for building a multi-turn
  • Carnegie Mellon University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    RubyStar is a dialog system designed to create “human-like” conversation by combining different response generation strategies. RubyStar conducts a non- task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation
  • Seoul National University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this work, we designed a conversational system by combining a finite state method, a retrieval model and a machine-initiative dialogue strategy. By using the machine-initiative strategy, most of the user queries are handled by the finite state machine which models predefined dialogues. If the user’s utterance is out of the range of modeled dialogues, the retrieval model processes the input. On comparative
  • University of California, Santa Cruz
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We present our modular system, highlighting our different data sources and how we use the human mind as a model for data management. Additionally we build and employ natural language understanding and information retrieval tools
  • University of Montreal
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We present a novel, large-scale ensemble-based system for the Amazon Alexa Prize competition. Our system leverages state-of-the-art methods from deep learning and reinforcement learning. We carry out A/B testing experiments with real-world users and demonstrate that our approach yields substantial improvements over several baseline systems. During the competition semi-finals, our best performing system
  • University of Edinburgh
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training
  • University of Washington
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    This paper introduces the University of Washington’s Alexa Prize socialbot, Sound- ing Board, which is designed to engage users with a wide variety of content. The system models the user utterance using a multi-dimensional representation. A hierarchical dialogue manager is employed where a master manages the overall conversation and a collection of miniskills manage different conversation segments. The
  • University of Trento
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Open–domain dialogue systems should be able to cover a very large set of domains and at the same time keep the user engaged in the interaction. Current approaches to dialogue modeling are divided between domain–independent, non–modular approaches using sequence–to–sequence models and the domain–specific modular systems developed for task–based dialogue. Furthermore, user engagement in dialogue, addressed
  • Emory University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We describe an information-oriented conversational AI system, EmersonBot, developed for the Alexa Prize 2017 competition. The main goal of the system was informing users about current events, and answering their questions, while maintaining a fluent conversation. The main innovations of Emersonbot include the development of a federated multi-source information retrieval system that is aware of the conversation
  • Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this
  • Princeton University
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    We present Pixie, a socialbot submitted to the 2017 Alexa Prize, developed with the objective of holding engaging casual conversations on open-ended topics. We discuss the modular, heterogeneous architecture of the Pixie system, and highlight the successes and shortcomings of our primarily template-based approach. We reflect on our findings about alternative paradigms used in the prototyping phase; in particular
  • Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In recent years, chat-bots that assist people’s daily life gained tremendous popular- ity, such as Siri, Cortana, Alexia and Google assistant. Typically, conversations with chat-bots are task orientated and brief. In this work, we explore creating social bot that can complete task and entertain user. We proposed a two-layered dialogue framework which allows for flexible reuse and reorganization of individual
  • University of California, Berkeley
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    In this document we outline the data collection methods, system, and experiments we ran during the course of this project. We aim to shed light on what drove the development of our bot and the techniques we used to power our bot in the course of the conversation.
  • Czech Technical University in Prague
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition
  • Lucia Santamaria, Amittai Axelrod
    IWSLT 2017
    2017
    We present and apply two methods for addressing the problem of selecting relevant training data out of a general pool for use in tasks such as machine translation. Building on existing work on class-based language difference models [1], we first introduce a cluster-based method that uses Brown clusters to condense the vocabulary of the corpora. Secondly, we implement the cynical data selection method [2
  • Social Science Research Network
    2017
    We study the problem of a retailer that maximizes profit through joint replenishment, pricing and removal decisions. This problem is motivated by the observation that retailers usually retain rights to remove inventory from their network either by returning it to the suppliers or through liquidation in the face of random demand and capacity constraints. We develop a tractable dynamic program by leveraging
  • NeurIPS 2017
    2017
    We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, forking-sequences, is designed for sequential
  • Javier González, Zhenwen Dai, Andreas Damianou, Neil Lawrence
    ICML 2017
    2017
    Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive. In this paper we consider the case where direct access to the function is not possible, but information about user preferences is. Such scenarios arise in problems where human preferences are modeled, such as A/B tests or recommender
IN, TS, Hyderabad
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. - Mentor and guide team of Applied Scientists from technical and project advancement stand point - Contribute research to science community and conference quality level papers.
US, WA, Seattle
We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact? The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards. As a Senior Applied Scientist on our Security Tooling team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality. Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity. Key job responsibilities • Design and implement novel AI/ML solutions for complex security challenges and improve builder experience • Drive advancements in machine learning and science • Balance theoretical knowledge with practical implementation • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results • Establish best practices for ML experimentation, evaluation, development and deployment You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life • Integrate ML models into production security tooling with engineering teams • Build and refine ML models and LLM-based agentic systems that understand builder intent • Create agentic AI solutions that reduce security friction while maintaining high security standards • Prototype LLM-powered features that automate repetitive security tasks • Design and conduct experiments (A/B tests, observational studies) to measure downstream impacts of tooling changes on engineering productivity • Present experimental results and recommendations to leadership and cross-functional teams • Gather feedback from builder communities to validate hypotheses About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
SE, Stockholm
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 Prime Video Sye Protocol team is looking for an Applied Scientist. This person will deliver features that automatically detect and prevent video quality issues before they reach millions of customers worldwide. You will lead the design of models that scale to very large quantities of video 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 engineering teams to deliver your research into production systems that ensure premium streaming experiences for customers globally. You will have demonstrated technical, teamwork and communication skills, and a motivation to deliver customer value from your research. Our team offers exceptional opportunities for you to grow your technical and non-technical skills and make a global impact. Key job responsibilities - Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement to solve complex video defect detection challenges. - Collaborate with software engineers to integrate successful experimental results into Prime Video wide processes and production systems that operate at scale with minimal computational overhead. - Communicate results and insights to both technical and non-technical audiences, including presentations and written reports to stakeholders across engineering, operations, and content teams. A day in the life Your typical day starts investigating overnight video quality alerts and developing breakthrough detection algorithms. You'll collaborate with engineering teams on production deployment, analyze video data to uncover quality patterns, and work with transformers and video language models. About the team You'll join a team focused on delivering premium video experiences through scientific innovation. We build machine learning systems that automatically detect video quality issues across our global streaming platform, collaborating closely with engineering, operations, and content teams to solve video analysis challenges while ensuring customers never experience poor quality. Our team partners with leading universities to develop solutions and advance computer vision and machine learning techniques. We value scientific rigor whilst staying customer-focused, encouraging both innovative and practical solutions that scale globally. There are opportunities for high-impact publications and patent development that advance the entire field.
US, MA, Boston
AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
US, VA, Arlington
Are you fascinated by the power of Large Language Models (LLM) and Artificial Intelligence (AI) to transform the way we learn and interact with technology? Are you passionate about applying advanced machine learning (ML) techniques to solve complex challenges in the cloud learning space? If so, AWS Training & Certification (T&C) team has an exciting opportunity for you as an Applied Scientist. At AWS T&C, we strive to be leaders in not only how we learn about the latest AI/ML development and AWS services, but also how the same technologies transform the way we learn about them. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences in our Skill Builder platform for both new learners and seasoned developers. You will be a part of a global team that is focused on transforming how people learn. The position will interact with global leaders and teams across the globe as well as different business and technical organizations. Join us at the AWS T&C Science Team and become a part of a global team that is redefining the future of cloud learning. With access to vast amounts of data, exciting new technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the ways how worldwide learners engage with our learning system and builders develop on our platform. Together, we will drive innovation, solve complex problems, and shape the future of future-generation cloud builders. Please visit https://skillbuilder.awsto learn more. Key job responsibilities - Apply your expertise in LLM to design, develop, and implement scalable machine learning solutions that address challenges in discovery and engagement for our international audiences. - Collaborate with cross-functional teams, including software engineers, data engineers, scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance operational performance and customer experiences across Skill Builder. - Continuously explore and evaluate state-of-the-art techniques and methodologies to improve the accuracy and efficiency of AI/ML systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. 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. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.
US, MA, N.reading
Amazon Industrial Robotics 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 Industrial Robotics 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. We are pioneering the development of robotics dexterous hands that: - Enable unprecedented generalization across diverse tasks - Are compliant and durable - Can span tasks from power grasps to fine dexterity and nonprehensile manipulation - Can navigate the uncertainty of the environment - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. 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 robust sensing for dexterous manipulation, including but not limited to: Tactile sensing, Position sensing, Force sensing, Non-contact sensing - Prototype the various identified sensing strategies, considering the constraints of the rest of the hand design - Build and test full hand sensing prototypes to validate the performance of the solution - Develop testing and validation strategies, supporting fast integration into the rest of the robot - Partner with cross-functional teams to iterate on concepts and prototypes - Work with Amazon's robotics engineering and operations customers to deeply understand their requirements and develop tailored solutions - Document the designs, performance, and validation of the final system
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team. As a Senior Applied Scientist, you'll spearhead the development of breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as locomotion, manipulation, sim2real transfer, multi-modal and multi-task robot learning, designing novel frameworks that bridge the gap between research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives across the robotics stack, driving breakthrough approaches through hands-on research and development in areas including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, and sim-to-real transfer, as well as foundation models for perception and manipulation - Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development - Develop and optimize control algorithms and sensing pipelines that enable robust performance in production environments - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack - Influence technical decisions and implementation strategies within your area of focus A day in the life - Design and implement innovative systems and algorithms, working hands-on with our extensive infrastructure to prototype and evaluate at scale - Guide fellow scientists in solving complex technical challenges across the full robotics stack - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster and extensive robotics infrastructure - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as locomotion, manipulation, sim2real transfer, multi-modal and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, and sim-to-real transfer, as well as foundation models for perception and manipulation - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development - Develop and optimize control algorithms and sensing pipelines that enable robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through ground breaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the International Emerging Stores organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team Central Machine Learning team works closely with the IES business and engineering teams in building ML solutions that create an impact for Emerging Marketplaces. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on millions of consumers and end users.
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Amazon continues to develop its advertising program. Ads run in our Stores (including Consumer Stores, Books, Amazon Business, Whole Foods Market, and Fresh) and Media and Entertainment publishers (including Fire TV, Fire Tablets, Kindle, Alexa, Twitch, Prime Video, Freevee, Amazon Music, MiniTV, Audible, IMDb, and others). In addition to these first-party (1P) publishers, we also deliver ads on third-party (3P) publishers. We have a number of ad products, including Sponsored Products and Sponsored Brands, display and video products for smaller brands, including Sponsored Display and Sponsored TV. We also operate ad tech products, including Amazon Marketing Cloud (a clean-room for advertisers), Amazon Publisher Cloud (a clean-room for publishers), and Amazon DSP (an enterprise-level buying tool that brings together our ad tech for buying video, audio, and display ads). Key job responsibilities This role is focused on developing core models that will be the foundational of the core advertising-facing tools that we are launching. You will conduct literature reviews to stay on the current news in the field. You will regularly engage with product managers and technical program managers, who will partner with you to productize your work.