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18,482 results found
  • Nikolaos Aletras, Arpit Mittal
    ECIR 2017
    2017
    Topics generated by topic models are usually represented by lists of t terms or alternatively using short phrases or images. The current state-of-the-art work on labeling topics using images selects images by re-ranking a small set of candidates for a given topic. In this paper, we present a more generic method that can estimate the degree of association between any arbitrary pair of an unseen topic and
  • Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias Seeger
    ICML 2017
    2017
    Bayesian optimization has been successfully used to optimize complex black-box functions whose evaluations are expensive. In many applications, like in deep learning and predictive analytics, the optimization domain is itself complex and structured. In this work, we focus on use cases where this domain exhibits a known dependency structure. The benefit of leveraging this structure is twofold: we explore
  • Estimating the relevance of documents based on the user feedback is an essential component of search, retrieval and ranking problems. User click modeling in search has focused primarily on factoring out the position bias. It is easy to see that the query type (generic queries vs specific queries) and user intent (purchase vs exploration) also introduce a bias in the click signal. In other words, the results
  • Amittai Axelrod
    arXiv
    2017
    The Moore-Lewis method of “intelligent selection of language model training data” is very effective, cheap, efficient... and also has structural problems. (1) The method defines relevance by playing language models trained on the in-domain and the out-of-domain (or data pool) corpora against each other. This powerful idea – which we set out to preserve – treats the two corpora as the opposing ends of a
  • NeurIPS 2017
    2017
    Rapidly growing product lines and services require a finer-granularity forecast that considers geographic locales. However the open question remains, how to assess the quality of a spatio-temporal forecast?
  • VLDB 2017
    2017
    We present a platform built on large-scale, data-centric machine learning (ML) approaches, whose particular focus is demand forecasting in retail. At its core, this platform enables the training and application of probabilistic demand forecasting models, and provides convenient abstractions and support functionality for forecasting problems. The platform comprises of a complex end-to-end machine learning
  • Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post
    arXiv
    2017
    We describe SOCKEYE, 1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural
  • Ashwin Ram, Rohit Prasad, Chandra Khatri, Anushree Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue
    Alexa Prize SocialBot Grand Challenge 1 Proceedings
    2017
    Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents
  • Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post
    2017
    Sockeye is an open-source sequence-to-sequence framework for neural machine translation (NMT) built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. Recent developments and changes are tracked in our CHANGELOG. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014
  • David Dai, Mike Maas, Deepak Gokhale, David Zhang, Delin Davis, Piradeep Kandasamy, Kaifei Lei, Abhinav Miglani, Jason Xie , Henri Yandell, Mike Maas, Phil Freo
    2017
    You can build smart home and other products that customers can control from millions of Alexa devices with just their voice. Expand your device’s capabilities with Alexa to create delightful experiences for lights, switches, thermostats, cameras, locks, and more. Plus, your Alexa-connected devices continue to become smarter with Alexa’s growing list of smart home capabilities and features that leverage
  • University of Montreal
    Meet the Mila team from the University of Montreal, a French-language public research university in Montreal, Quebec, Canada.
  • University of California, Santa Cruz
    Meet the SluBot team from UC Santa Cruz, a public land-grant research university in Santa Cruz, California.
  • University of Trento
    Picture walking into your house after a long day. As you step in, your favorite music starts playing in the background and the lights dim to suit the weather outside.
  • Princeton University
    We're an eclectic team of research-oriented undergraduate and graduate students in Princeton's CS and math departments.
  • University of Washington
    Sounding Board is a conversational agent that engages users through thoughtful and informative discussion.
  • Emory University
    Meet the EmersonBot team from Emory University, a private research university in Atlanta, Georgia, US.
  • University of California, Berkeley
    The UC Berkeley team consists of William, Phillip, Piyush, and James, and is the only fully undergraduate team in the competition.
  • Carnegie Mellon University
    This team, consisting of PhD and Masters students from Carnegie Mellon University, brings together experience in chatbot interaction strategies, question answering, neural modeling and machine learning.
  • Czech Technical University in Prague
    We are the Alquist team from CTU, Prague, Czech Republic.
  • University of Edinburgh
    We are Edina, from the University of Edinburgh, a world-leading institution in Artificial Intelligence.
US, CA, Sunnyvale
Our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Senior Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services .
US, CA, Santa Clara
The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You'll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you'll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: * AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization * Machine Learning Compiler: Creating advanced compiler techniques for ML workloads * System Robustness: Building tools for accuracy and reliability validation * Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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.
US, VA, Arlington
As a Survey Research Scientist within the Reputation Marketing & Insights team, your primary responsibility will be to help manage our employee communications research program, including a global tracking survey. The work will challenge you to be resourceful, think big while staying connected to the details, translate survey, focus group results, and advanced analytics into strategic direction, and embrace a high degree of change and ambiguity at speed. The scope and scale of what we strive to achieve is immense, but it is also meaningful and energizing. This is an individual contributor role. The right candidate possesses endless curiosity and passion for understanding employee perceptions and what drives them. You have end-to-end experience conducting qualitative research, robust large-scale surveys, campaign measurement, as well as advanced modeling skills to uncover perception drivers. You have proficiency in diving deep into large amounts of data and translating research into actionable insights/recommendations for internal communicators. You are an excellent writer who can effectively communicate data-driven insights and recommendations through written documents, presentations, and other internal communication channels. You are a creative problem-solver who seeks to deeply understand the business/communications so you can tailor research that informs stakeholder decision making and strategic messaging tactics. Key job responsibilities - Design and manage the execution of a global tracking survey focused on employee communications - Develop research to identify and test messages to drive employee perceptions - Use advanced statistical methodologies to better understand the relationship between key internal communications metrics and other related measures of perception (e.g., regression, structural equation modeling, latent growth curve modeling, Shapley analysis, etc.) - Develop causal and semi-causal measurement techniques to evaluate the perception impact of internal communications campaigns - Identify opportunities to simplify existing research processes and operate more nimbly - Engage in strategic discussions with internal partner teams to ensure our research generates actionable and on-point findings About the team This team sits within the CCR organization. Our focus is on conducting research that identifies messaging opportunities and informs communication strategies for Amazon as a brand.
US, CA, Sunnyvale
We're seeking an Applied Scientist to pioneer sensor-based algorithms that power next-generation experiences across Amazon's device ecosystem, including Echo, Kindle, Fire TV, and Fire Tablets. Working with multidisciplinary teams of scientists and engineers, you'll develop innovative technologies at the intersection of signal processing and machine learning that transform how millions of customers interact with our products. The ideal candidate combines strong theoretical foundations in machine learning and signal processing with practical implementation skills. You'll develop state-of-the-art sensor algorithms from concept to production, translate complex research problems into practical consumer technologies, and create solutions optimized for diverse hardware platforms. We're looking for someone who thrives in fast-paced environments, solves complex problems efficiently, and iterates quickly based on real-world feedback. Your technical decisions will directly shape future product capabilities and deliver exceptional experiences to Amazon customers worldwide. Key job responsibilities - Develop and implement advanced algorithms and machine learning models to enhance Amazon's products and services. - Collaborate with cross-functional teams, including software engineers, scientists, and product managers to translate business needs into technical solutions. - Conduct thorough data analysis to identify trends, patterns, and insights that drive product innovation and improvement. - Optimize algorithms for performance, scalability, and efficiency across various Amazon platforms. - Present findings and recommendations to stakeholders, influencing product strategy and decision-making. - Stay abreast of the latest research and technological advancements in machine learning and related fields to continuously improve Amazon's offerings. - Ensure the ethical use of data and algorithms, adhering to Amazon's guidelines and best practices. - Contribute to the publication of research findings in conferences and journals, elevating Amazon's reputation in the scientific community. About the team At Amazon Lab126, we're a pioneering research and development hub dedicated to designing and engineering revolutionary consumer electronics. Established in 2004 as a subsidiary of Amazon.com, Inc., we've been at the forefront of innovation, starting with the creation of the best-selling Kindle family of products. Our portfolio has since expanded to include transformative devices such as Fire tablets, Fire TV, and Amazon Echo. Our Lab126 team is dedicated to developing advanced sensing technologies and algorithms, collaborating with program managers to design and implement transformative user features and experiences.
US, CA, Sunnyvale
Are you passionate about solving complex wireless challenges that impact millions of customers? Join Amazon's Device Connectivity team who are revolutionizing how wireless technology shapes the future of consumer electronics. As a Wireless Research Scientist, you'll be at the forefront of developing solutions that enhance the connectivity and reliability of millions of customer devices. Your expertise will drive the creation of next-generation wireless technologies, from concept to implementation, directly shaping the future of Amazon's product ecosystem. In this role, you'll tackle complex electromagnetic challenges head-on, leveraging your analytical prowess and deep understanding of wireless principles. You'll collaborate with world-class scientists and engineers, applying machine learning and statistical analysis to optimize system performance and create scalable, cost-effective solutions for mass production. Your impact will extend beyond the lab, as you transform research concepts into practical features that delight our customers. You'll influence product roadmaps, drive critical technical decisions, and play a key role in accelerating our product development lifecycle. Key job responsibilities As a Wireless research scientist, you will use your experience to initiate wireless design, development, execution and implementation of scientific research projects. Working closely with fellow hardware dev, scientists and product managers, you will use your experience in modeling, statistics, and simulation to design new hardware, customer modeling and evaluate their benefits and impacts to cost, connectivity use cases, reliability, and speed of productization Ability to work and connect concepts across various engineering fields like EMC design, desense, antenna, wireless communication and computational electromagnetics to solve complex and novel problems Experience in combinatorial optimization, algorithms, data structures, statistics, and/or machine learning that can be leveraged to develop novel wireless designs that can be integrated and mass produced on products. This position requires superior analytical thinking, and ability to apply their technical and statistical knowledge to identify opportunities for wireless/EM applications. You should be able to mine and analyze large data, and be able to use necessary programming and statistical analysis software/tools to do so. Ability to leverage ML techniques for design optimization and performance modeling that influence technology integration and productization of novel consumer products. A day in the life Invent • You invent and design new solutions for scientifically-complex problem areas and identify opportunities for invention in existing or new business initiatives. • You expertly frame the scientific approach to solve ambiguous business problems, distinguishing between those that require new solutions and those that can be addressed with existing approaches. • You focus on business and customer problems that require scientific advances at the product level. Your research solutions set a strong example for others. You work efficiently and routinely delivered the right things. • You show good judgment when making trade-offs between short- and long-term customer, business, and technology needs. • You drive your team’s scientific agenda by proposing new initiatives and securing management buy-in. • You lead the writing of internal documents or external publications when appropriate for your team and not precluded by business considerations. • Your work consistently delivers significant benefit to the business. What you deliver could be functional, such as a software system or conceptual, such as a paper that advances scientific knowledge in a specific field or convinces the business to focus on a particular strategy. Implement • You are self-directed in your daily work and require only limited guidance for confidence checks. • You define and prioritize science or engineering specifications for new approaches. • You independently assess alternative technologies or approaches to choose the right one to be used by your system or solution with little guidance. You may own the delivery of solutions for an entire business application. • You ensure accuracy in your process abstractions, models, and simulation results. • Your solutions are inventive, maintainable, scalable, extensible, accurate, and cost-effective (e.g., you know where to extend or adapt methods). • Your solutions are creative and of such a high quality that they can be handed off with minimal rework. Influence • You are a key influencer in team strategy that impacts the business. You make insightful contributions to team roadmaps, goals, priorities, and approach. • You build consensus on larger projects and factor complex efforts into independent tasks that can be performed by you and others. • You actively recruit and help others by coaching and mentoring in your organization (or at your location). • You are involved and visible in the broader scientific communities (internal or external) as a subject matter expert. For example, you may give guest lectures, review scientific work of others, serve as a Program Committee member in conferences, or serve as a reviewer for journal publications. • You contribute to the broader internal and external scientific communities. About the team 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 innovative devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?
US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
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! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences.
US, CA, Santa Clara
Want to work on frontier, world class, AI-powered experiences for health customers and health providers? The Health Science & Analytics group in Amazon's Health Store & Technology organization is looking for a Senior Manager of Applied Science to lead a group of applied scientists and engineers to work hand in hand with physicians to build the future of AI-powered healthcare experiences. We have an ambitious roadmap which includes scaling recently launched products which are already delighting products and the opportunity to build disruptive, new experiences. This role will be responsible for leading the science and technology teams driving these key innovations on behalf of our customers. Key job responsibilities - Independently manage a team of scientists and engineers to sustainably deliver science driven products. - Define the vision and long-term technical roadmap to achieve multi-year business objectives. - Maintain and raise the science bar of the team’s deliverables and keep the broader Amazon Health Services organization apprised of the latest relevant technical developments in the field. - Work across business, clinical, and technical leaders to disambiguate product requirements and socialize progress towards key goals and deliverables. - Proactively identify risks and shape the technical roadmap in anticipation of industry trends in emerging AI subfields.
CA, BC, Vancouver
Have you ever wondered how Amazon predicts delivery times and ensures your orders arrive exactly when promised? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's multimodal logistics network that includes planes, trucks, and vans sound exciting to you? Are you interested in developing Generative AI solutions using state-of-the-art LLM techniques to revolutionize how Amazon optimizes the fulfillment of millions of customer orders globally with unprecedented scale and precision? If so, then we want to talk with you! Join our team to apply the latest advancements in Generative AI to enhance our capability and speed of decision making. Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfillment Optimization owns and operates optimization, machine learning, and simulation systems that continually optimize the fulfillment of millions of products across Amazon’s network in the most cost-effective manner, utilizing large scale optimization, advanced machine learning techniques, big data technologies, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing, and supply. The team has embarked on its Generative AI to build the next-generation AI agents and LLM frameworks to promote efficiency and improve productivity. We’re looking for a passionate, results-oriented, and inventive machine learning scientist who can design, build, and improve models for our outbound transportation planning systems. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create ML / AI solutions to solve those problems at scale. You will work independently in an ambiguous environment while collaborating with cross-functional teams to drive forward innovation in the Generative AI space. Key job responsibilities * Design, develop, and evaluate tailored ML/AI, models for solving complex business problems. * Research and apply the latest ML / AI techniques and best practices from both academia and industry. * Identify and implement novel Generative AI use cases to deliver value. * Design and implement Generative AI and LLM solutions to accelerate development and provide intuitive explainability of complex science models. * Develop and implement frameworks for evaluation, validation, and benchmarking AI agents and LLM frameworks. * Think about customers and how to improve the customer delivery experience. * Use analytical techniques to create scalable solutions for business problems. * Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at large scale. * Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. A day in the life You will have the opportunity to learn how Amazon plans for and executes within its logistics network including Fulfillment Centers, Sort Centers, and Delivery Stations. In this role, you will design and develop Machine Learning / AI models with significant scope, impact, and high visibility. You will focus on designing, developing, and deploying Generative AI solutions at scale that will improve efficiency, increase productivity, accelerate development, automate manual tasks, and deliver value to our internal customers. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide at a scale that is unique to Amazon. We own the long-term and intermediate-term planning of Amazon’s global fulfillment centers and transportation network as well as the short-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfillment network. FPX science team is a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across SCOT - Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We disambiguate complex supply chain problems and create innovative data-driven solutions to solve those problems at scale with a mix of science-based techniques including Operations Research, Simulation, Machine Learning, and AI to tackle some of our biggest technical challenges. In addition, we are incorporating the latest advances in Generative AI and LLM techniques in how we design, develop, enhance, and interpret the results of these science models.
US, NY, New York
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 Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. 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 - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - 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; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.