https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning * Research areas + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability * Blog * News and features + Awards and recognitions + Awards and recognitions * Publications * Conferences * Collaborations + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE * Careers + Internships + Working at Amazon + Internships + Working at Amazon [] Feedback Follow Us * twitter * instagram * youtube * facebook * linkedin Menu amazon-science-logo.svg * Research areas + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability * Blog * News and features + Awards and recognitions + Awards and recognitions * Publications * Conferences * Collaborations + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE * Careers + Internships + Working at Amazon + Internships + Working at Amazon [] Feedback Search [ ] Submit Search Conversational AI / Natural-language processing 20B-parameter Alexa model sets new marks in few-shot learning With an encoder-decoder architecture -- rather than decoder only -- the Alexa Teacher Model excels other large language models on few-shot tasks such as summarization and machine translation. By Saleh Soltan August 02, 2022 Share Share * Copy link * Email * Twitter * LinkedIn * Facebook * WhatsApp * Reddit * QZone * Sina Weibo Related publications * AlexaTM 20B: Few-shot learning using a large-scale multilingual seq2seq model Most major advances in AI have come from supervised learning, in which machine learning models are trained on annotated data. But as commercial AI models continue to increase in scale, relying on data annotation is becoming unsustainable. At Alexa AI, we are moving to the new paradigm of generalizable intelligence, in which models can learn new concepts and transfer knowledge from one language or task to another with minimal human input. Such models allow us to efficiently develop new features and improve Alexa on multiple languages at the same time. As part of this move, we have introduced Transformer-based large-scale multilingual language models we call Alexa Teacher Models (AlexaTM). Given only a few examples of a task in a new language, AlexaTM can transfer what it knows to the new language with no extra human supervision. Centroid core set.png Related content Simplifying BERT-based models to increase efficiency, capacity New method would enable BERT-based natural-language-processing models to handle longer text strings, run in resource-constrained settings -- or sometimes both. In a paper we're presenting at this year's Knowledge Discovery and Data Mining Conference (KDD), we showed that 10-billion- and two-billion-parameter AlexaTM models can improve on state-of-art cross-lingual transfer learning and increase Alexa's accuracy in different locales. In a follow-up paper, which we're publishing on arXiv later today, we have taken this line of research a step further, with a 20-billion-parameter generative model called AlexaTM 20B. The experiments reported in the paper -- which use only publicly available data -- show that AlexaTM 20B can not only transfer what it learns across languages but also learn new tasks from just a handful of examples (few-shot learning). In the example below, the model is provided with three examples of different intents, or tasks that the customer wants executed: book-restaurant, play-music, and get-weather. The model can generalize from these to the unfamiliar intent get-news-update and generate utterances corresponding to that intent in different languages. This allows us to develop new features more rapidly, and in multiple languages, simultaneously. Multilingual annotation.png Using AlexaTM 20B to generate annotated data for a new intent in different languages. Our work is inspired by the recent work by OpenAI and development of GPT-3 model. However, where other large language models use decoder-only architectures, AlexaTM 20B model is a sequence-to-sequence (seq2seq) encoder-decoder. In an encoder-decoder architecture, the encoder produces a representation of an input text using a bidirectional encoding, and the decoder uses that representation to perform some task -- historically, generating translation of the input. By contrast, the decoder-only model uses left-to-right (unidirectional) encoding of the input text. This works well for language modeling, in which the task is to predict the next token in a sequence based on those that precede it, but it's less effective for machine translation and text summarization, the tasks on which AlexaTM 20B outperforms GPT-3. AlexaTM 20B also tops GPT-3 by being multilingual, supporting Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu. And its carbon footprint during training is only one-fifth of GPT-3's, thanks to its lower parameter count and internal improvements to our training engine. Graphic of Agora sampling the development set and generating, labeling and adding new points back to the training set. Related content A version of the BERT language model that's 20 times as fast Determining the optimal architectural parameters reduces network size by 84% while improving performance on natural-language-understanding tasks. To train AlexaTM 20B, we break with convention, training on a mix of denoising and causal-language-modeling (CLM) tasks. On the denoising task, the model is required to find dropped spans and generate the complete version of the input. This is similar to how other seq2seq models like T5 and BART are trained. On the CLM task, the model is required to meaningfully continue the input text. This is similar to how decoder-only models like GPT-3 and PaLM are trained. Training on a mix of these two pretraining tasks enables AlexaTM 20B to generalize based on the given input and generate new text (the CLM task), while also performing well on tasks that seq2seq models are particularly good at, such as summarization and machine translation (the denoising task). Pre-training objectives.png AlexaTM 20B pre-training objectives. During pretraining, the model is trained on the denoising task 80% of the time and on causal language modeling (CLM) 20% of the time. For example, we demonstrated that, given a single article-summarization pair, AlexaTM 20B can generate higher-quality summaries in English, German, and Spanish than the much larger PaLM 540B can (see example, below). Biased language models still.jpg Related content New dataset, metrics enable evaluation of bias in language models Human-evaluation studies validate metrics, and experiments show evidence of bias in popular language models. Moreover, AlexaTM 20B achieves state-of-the-art performance in few-shot machine translation (MT) across almost all language pairs supported by the model on the Flores-101 dataset. The gains in translating to and from low-resource languages like Marathi, Tamil, and Telugu are particularly significant (e.g., 21.8 Arabic-to-Tamil sentence-piece BLEU score compared to 0.9 for the supervised M2M-124 615M model). These results suggest that large-scale seq2seq-style pretraining, as formulated in our work, improves MT for languages with few training pairs, especially when a large amount of monolingual data is available for the target language. AlexaTM 20B has no difficulty translating directly from different languages, in contrast to many-to-many MT systems that require parallel translation data for training. News summarization.png News summarization by AlexaTM 20B when given only a single example. The input to the encoder is in the yellow box, the decoder's output in the pink box. AlexaTM 20B is the largest multilingual seq2seq model to date that is also capable of few-shot learning. We will be releasing the model publicly for non-commercial use to aid the development and evaluation of multilingual large language models (LLMs). We have also implemented a function to enable loading the model on up to eight GPUs with limited GPU memory for running inference on instances of Amazon Web Services' EC2 computation service. This provides a more flexible way for researchers to use AlexaTM 20B in their own work. In an analysis reported in our paper, we found that AlexaTM 20B, like other LLMs, has some likelihood of reproducing toxic language, social biases, and harmful stereotypes found in its training data. Therefore, we recommend that users conduct a full task-specific fairness-and-bias analysis before using the model to fully understand and address any potential harm that might arise from its use. Depending on the downstream application that AlexaTM 20B is being applied to, one or several of the prior techniques from the literature might be used to detoxify and debias the model. We reiterate the importance of task-specific fairness auditing and emphasize the need for more research on bias measurement and mitigation from the community. All in all, we demonstrated in our work that the proposed style of pretraining enables seq2seq models that outperform much larger decoder-only LLMs across different tasks, both in a few-shot setting and with fine-tuning. We hope our work presents a compelling case for seq2seq models as a powerful alternative to decoder-only models for LLM training. Research areas * Conversational AI / Natural-language processing Tags * Alexa * Language models * Few-shot learning Related publications * AlexaTM 20B: Few-shot learning using a large-scale multilingual seq2seq model About the Author Saleh Soltan Saleh Soltan is a senior applied scientist with Alexa AI. Related content * Federated Learning Animation.gif Advances in trustworthy machine learning at Alexa AI Christophe Dupuy, Jwala Dhamala, Rahul Gupta April 28, 2022 The team's latest research on privacy-preserving machine learning, federated learning, and bias mitigation. Conversational AI / Natural-language processing * Control knobs 16|9.png Controlling language generation models without training data Larry Hardesty July 02, 2021 Giving a neural generation model "control knobs" enables modulation of the content of generated language. Conversational AI / Natural-language processing * AmazonScience_StaticGraphic Improving question-answering models that use data from tables Patrick Ng February 28, 2022 Novel pretraining method enables increases of 5% to 14% on five different evaluation metrics. Search and information retrieval Work with us See more jobs See more jobs Applied Scientist- AWS AI US, CA, Santa Clara Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon's customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon's heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/ Life BalanceOur team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Senior Data Scientist US, WA, Seattle Job summaryAmazon Sub-Same-Day Supply Chain team is looking for an experienced and motivated Senior Data Scientist to generate data-driven insights influencing the long term SSD supply chain strategy, build the necessary predictive models, optimization algorithms and customer behavioral segments allowing us to discover and build the roadmap for SSD to enable operational efficiency and scale.Key job responsibilitiesWork with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems.Translate business problems into specific analytical questions and form hypotheses that can be answered with available data using scientific methods or identify additional data needed in the master datasets to fill any gapsDesign, develop, and evaluate highly innovative statistics and ML modelsGuide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationProactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.A day in the lifeIn this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Business Engineers, and other Scientists, to deeply understand SSDs current optimization strategy while benchmarking against industry best practices and standards to gain insights that will drive our roadmap. A successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.About the teamAmazon's Sub-Same Day (SSD) delivery program is designed to get customers their items as fast as possible - currently in as quickly as five hours. With ultra-fast delivery becoming increasingly important, we are looking for an experienced Senior Data Scientist to help us benchmark against industry standards to uncover insights to improve and optimize the long term supply chain strategy for Amazons Sub-Same-Day business. Applied Scientist- AWS AI US, NY, New York Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon's customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon's heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/ Life BalanceOur team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Research Scientist II, Funnel Science and Analytics US, WA, Seattle Job summaryWorkforce Staffing (WFS) brings together the workforce powering Amazon's ability to delight customers: the Amazon Associate. With over 1M hires, WFS supports sourcing, hiring, and developing the best talent to work in our fulfillment centers, sortation centers, delivery stations, shopping sites, Prime Air locations, and more.WFS' Funnel Science and Analytics team is looking for a Research Scientist. This individual will be responsible for conducting experiments and evaluating the impact of interventions when conducting experiments is not feasible. The perfect candidate will have the applied experience and the theoretical knowledge of policy evaluation and conducting field studies.Key job responsibilitiesAs a Research Scientist (RS), you will do causal inference, design studies and experiments, leverage data science workflows, build predictive models, conduct simulations, create visualizations, and influence science and analytics practice across the organization.Provide insights by analyzing historical data from databases (Redshift, SQL Server, Oracle DW, and Salesforce).Identify useful research avenues for increasing candidate conversion, test, and create well written documents to communicate to technical and non-technical audiences.About the teamFunnel Science and Analytics team finds ways to maximize the conversion and early retention of every candidate who wants to be an Amazon Associate. By focusing on our candidates, we improve candidate and business outcomes, and Amazon takes a step closer to being Earth's Best Employer. Applied Scientist US, Virtual Job summaryAmazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers. We are seeking a skilled Applied Scientist to help us build the future of experimentation systems at Amazon.About you:You have an entrepreneurial spirit and want to make a big impact on Amazon and its customers. You are excited about cutting-edge research on unsupervised learning, graph algorithms, and causal inference in the intersection between Machine Learning, Statistics, and Econometrics. You enjoy building massive scale and high performance systems but also have a bias for delivering simple solutions to complex problems. You're looking for a career where you'll be able to build, to deliver, and to impress. You challenge yourself and others to come up with better solutions. You develop strong working relationships and thrive in a collaborative team environment.About us together:We're going to help Amazon make better long term decisions by designing and delivering A/B-testing systems for long-term experiments, and by using these systems to figure out how near term behavior impacts long term growth and profitability. Our work will inform some of the biggest decisions at Amazon. Along the way, we're going to face seemingly insurmountable challenges. We're going to argue about how to solve them, and we'll work together to find a solution that is better than each of the proposals we came in with. We'll make tough decisions, but we'll all understand why. We'll be the dream team.We have decades of combined experience on the team in many areas science and engineering so it's a great environment in which to learn and grow. A/B testing is one of the hottest areas of research and development in the world today and this is a chance to learn how it works in the company known for pioneering its use. Applied Scientist, AI Research & Education US, CA, Santa Clara Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/ principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs. Applied Scientist, AI Research & Education US, CA, Santa Clara Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/ principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs. Senior Applied Scientist - Machine Learning, Personalization, Recommendations, Machine Learning, Causal Inference, Personalization US, WA, Seattle Job summaryHow can we create a rich, data-driven shopping experience on Amazon? How do we build data models that helps us innovate different ways to enhance customer experience? How do we combine the world's greatest online shopping dataset with Amazon's computing power to create models that deeply understand our customers? Recommendations at Amazon is a way to help customers discover products. Our team's stated mission is to "grow each customer's relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". We strive to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon's large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.Key job responsibilitiesScaling state of the art techniques to Amazon-scaleWorking independently and collaborating with SDEs to deploy models to productionDeveloping long-term roadmaps for the team's scientific agendaDesigning experiments to measure business impact of the team's effortsMentoring scientists in the departmentContributing back to the machine learning science community Applied Scientist US, NY, New York City Job summaryAmazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/ guardduty/) and Macie (https://aws.amazon.com/macie/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop and enable them to take on more complex tasks in the future.A day in the lifeAbout the hiring groupJob responsibilities* Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.* Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.* Report results in a scientifically rigorous way.* Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for. Senior Applied Scientist, Media Planning US, CO, Boulder Job summaryAmazon 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.Our Media Planning team builds full funnel solutions for advertising agencies and publishers. Our suite of tools include data pipelines, machine learning models, large scale data structures and indexes, advertiser recommendations and data visualizations. We match supply (human eyeballs) and demand (advertisers interests) in thousands of audience targeting dimensions, and recommend optimal prices.As a Senior Applied Scientist on this team, you will: Be the technical leader in Machine Learning; lead efforts within this team and across other teams.Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Research new and innovative machine learning approaches.Recruit Applied Scientists to the team and provide mentorship.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE Key job responsibilities Be the technical leader in Machine Learning; lead efforts within this team and across other teams. Perform hands-on analysis and modeling of enormous data sets to develop insights that help brands grow on all platforms. Build Machine Learning services that build over multiple other underlying machine learning systems, by actively collaborating with a degree of ambiguity, scale, complexity. Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models into services. Understand the user story, and scope the best product choices using A/B experiments, metrics, and surveys. Establish scalable, efficient, automated processes for large-scale data analysis over multiple data pipelines spanning the entirety of the advertising business models, machine-learning model development, model validation and serving. Research and build simulators that work in tandem with innovative machine learning approaches. Recruit Applied Scientists to the team and provide mentorship.A day in the lifeImpact and Career Growth: You will invent new experiences and influence how businesses grow on Amazon and beyond; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our Media Planning business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Video Link: https:// www.linkedin.com/posts/ trisha-mahajan_meet-trisha-a-technical-program-manager-activity-6909894593551446016-lPYu? utm_source=linkedin_share&utm_medium=member_desktop_webAbout the teamWhy you will love this opportunity: Amazon is a new initiative to include Media Planning services to our existing world-class advertising business. This team defines and delivers a collection of media planning services that drive discovery and sales. Our solutions in expected to unlock billions in revenue and drive long-term growth for Amazon's advertising business. Amazon advertising deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated to make a dent in the universe by building a new vertical in Amazon advertising while having fun. The team has a broad mandate to experiment and innovate.Join the MADS Planning (MADS-P) team. We have a lot exciting new opportunities in Video advertising, Audio Ads, and Media Planning; big career growth opportunities.Please reach out for details! [amazon-science-logo-whi] * About * Research areas * Blog * News and features * Publications * Conferences * Collaborations * Careers * Alexa Prize * Academics * Research Awards * Amazon Developer * Amazon Web Services * About Amazon * Newsletter * RSS View from space of a connected network around planet Earth representing the Internet of Things. Have feedback for Amazon Science? We want to hear from you. 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