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Conversational AI

Building software and systems that help people communicate with computers naturally, as if communicating with family and friends.
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  • An empirical investigation towards efficient multi-domain language model pre-training
    Kristjan Arumae, Qing Sun, Parminder Bhatia
    EMNLP 2020
    2020
    Pre-training large language models has become a standard in the natural language processing community. Such models are pretrained on generic data (e.g. BookCorpus and English Wikipedia) and often fine-tuned on tasks in the same domain. However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training
    Conversational AI
  • Don’t use English dev: On the zero-shot cross-lingual evaluation of contextual embeddings
    Phillip Keung, Yichao Lu, Julian Salazar, Vikas Bhardwaj
    EMNLP 2020
    2020
    Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of
    Conversational AI
  • The Multilingual Amazon Reviews Corpus
    Phillip Keung, Yichao Lu, Gyorgy Szarvas, Noah Smith
    EMNLP 2020
    2020
    We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product
    Conversational AI
  • Don’t parse, insert: Multilingual semantic parsing with insertion based decoding
    Qile Zhu, Haidar Khan, Saleh Soltan, Stephen Rawls, Wael Hamza
    CoNLL 2020
    2020
    Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by the system. Many algorithms have been proposed to solve this problem, from conventional rulebased or statistical slot-filling systems to shiftreduce based neural parsers. For complex parsing tasks, the state-of-the-art method
    Conversational AI
  • End-to-end slot alignment and recognition for cross-lingual NLU
    Weijia Xu, Batool Haider, Saab Mansour
    EMNLP 2020
    2020
    Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns
    Conversational AI
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Applied Scientist, Personalization
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
Manager, Applied Scientist
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
Applied Scientist, Optimization, Amazon Transportation
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
Sr. Applied Scientist, Prime Video Peak Forecasting
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! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
Senior Data Scientist - APS, Publisher Products
US, CA, Palo Alto
Amazon’s Advertising Technology team builds the technology infrastructure and ad serving systems to manage billions of advertising queries every day. The result is better quality advertising for publishers and more relevant ads for customers. In this organization you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading companies. Amazon Publisher Services (APS) helps publishers of all sizes and on all channels better monetize their content through effective advertising. APS unites publishers with advertisers across devices and media channels. We work with Amazon teams across the globe to solve complex problems for our customers. The end results are Amazon products that let publishers focus on what they do best - publishing. The APS Publisher Products Engineering team is responsible for building cloud-based advertising technology services that help Web, Mobile, Streaming TV broadcasters and Audio publishers grow their business. The engineering team focuses on unlocking our ad tech on the most impactful Desktop, mobile and Connected TV devices in the home, bringing real-time capabilities to this medium for the first time. As a successful Data Scientist in our team, · You are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You will collaborate directly with product managers, BIEs and our data infra team. · You will analyze large amounts of business data, automate and scale the analysis, and develop metrics (e.g., user recognition, ROAS, Share of Wallet) that will enable us to continually measure the impact of our initiatives and refine the product strategy. · Your analytical abilities, business understanding, and technical aptitude will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. · You will have direct exposure to senior leadership as we communicate results and provide scientific guidance to the business. Major responsibilities include: · Utilizing code (Apache, Spark, Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems. · Collaborate with product, BIEs, software developers, and business leaders to define product requirements and provide analytical support · Build customer-facing reporting to provide insights and metrics which track system performance · Influence the product strategy directly through your analytical insights · Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
Applied Scientist , Amazon
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
Economist , PXT Central Science
US, WA, Seattle
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for an economist with expertise in applying causal inference methods, especially experimental design to topics in labor, personnel, education, or behavioral economics. We are particularly interested in candidates with experience applying these skills to strategic problems with significant business and/or social policy impact. The candidate will work with economists and engineers to estimate and validate their models on large scale data, and will help business partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business and its workforce. We are looking for a creative thinker who can combine a strong economic toolbox with a desire to learn from others, and who knows how to execute and deliver on big ideas. Ideal candidates will own key inputs to all stages of research projects, including model development, survey administration, experimental design, and data analysis. They will be customer-centric, working closely with business partners to define key research questions, communicate scientific approaches and findings, listen to and incorporate partner feedback, and deliver successful solutions.
Sr. Data Scientist, Amazon Business
US, WA, Seattle
At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Flywheel Inputs Science team is looking for Sr. Scientist to excel at the flywheel inputs of selection, pricing and operations. Amazon Business (AB) represents an incredible opportunity to address a vast new market segment and customer base for Amazon. We are focused on building solutions that enable B2B customers to find, research, and buy products and services across multiple devices and marketplaces. The Amazon Business Science team owns the science for key AB problems including price setting, selection additions and operations optimization. Amazon Business is a fast growing business sector. We need the leaders who can think big and drive their vision into a reality. You will build the science models and the supporting structures needed to analyze, dive deep, and innovate the selection and pricing strategies. You will also have the opportunity to present findings to cross functional team partners to drive improvements. You will work closely with other Applied/Research/Data Scientists, Economists, Data Engineers, Software Development Engineers, Program Managers and Business Partners to solve challenging problems. You need be comfortable using intellect, curiosity and technical ability to develop innovative solutions to business problems. You need learn different aspects of the business and understand how to apply science and analytics to solve high impact business problems. You will be expected to provide clear and concise explanation to results and approaches as well as provide opinion and guidance on problem solving. The ideal candidate will have the Amazon leadership skills, proven ability to develop, enhance, automate, and manage science models from end to end. The ideal candidate will have data mining and modeling skills and will be comfortable facilitating idea creation and working from concept through to execution. The ideal candidate must have demonstrated ability to manage medium-scale automation and modeling projects, identify requirements and build methodology and tools that are mathematically grounded but also explainable operationally, apply technical skills allowing the models to adapt to changing attributes. We are hiring the right talent and will adjust the job family based on the interview results to fit you in one of the following job families: Data Science, Research Science, Applied Science, Economist and Machine Learning Engineer. Key job responsibilities • Contribute to AB Flywheel Inputs strategy development based on science models and data analysis • Develop models to measure long term impact of seller behaviors • Collaborate with product and engineering teams both within and outside of AB to launch selection and operations systems based on science and data. • Use optimization, statistical, machine learning and analytical techniques to create scalable solutions for business problems. • Design, development and evaluation of highly innovative models for forecast, optimization and experimentation. • Research, experiment and implement novel approaches. • Work closely with other scientists in the team and across teams. • Work and collaborate effectively with product managers and software engineering teams to build algorithms and models and integrate successful models and algorithms in production systems. • Use the best practices in science: data integrity, design, test, and implementation and documentation. • Mentor and guide junior members in the team. • Contribute to Amazon's Intellectual Property through patents and internal and external publications A day in the life The scientist will develop, enhance, automate, and manage science models from end to end. The scientist will also have the opportunity to present findings to cross functional team partners to drive improvements. He/she will work with other Applied/Research/Data Scientists, Economists, Data Engineers, Software Development Engineers, Program Managers and Business Partners to build analytical and science models. The scientist will be expected to provide clear and concise explanation to results and approaches as well as provide opinion and guidance on problem solving. About the team Amazon Business (AB) represents an incredible opportunity to address a vast new market segment and customer base for Amazon. We are focused on building solutions that enable B2B customers to find, research, and buy products and services across multiple devices and marketplaces. The Amazon Business Science team owns the science and analytics for key AB problems including price setting and selection additions.
Sr. Research Scientist, Fulfillment By Amazon (FBA) Science, FBA Science
US, WA, Bellevue
The Fulfillment by Amazon (FBA) and Supply Chain by Amazon (SCA) empower third-party sellers world-wide to use Amazon's advanced logistics, warehousing, distribution, fulfillment, and transportation services. These services ensure products remain in stock, customer orders are fulfilled fast and reliably, while end-to-end supply chain costs are reduced. As a result, sellers can focus less on supply chain management and more on developing products, delighting customers, and scaling their businesses. The FBA team is looking for a passionate, curious, and creative Research Scientist with expertise in operations research, machine learning or statistics, preferably with a focus on pricing and market design, and a proven record of solving business problems through scalable modeling and analytical skills. As a research scientist in the team, you will be responsible for designing and implementing optimization and ML models, building automated inventory, logistics, capacity and revenue management systems while collaborating with business and software teams to solve key challenges facing the worldwide FBA business. Such challenges include 1) designing end-to-end supply and demand management systems, 2) developing and improving optimization and ML models to support FBA sellers in growing their businesses, 3) creating decision-support tools that minimize the cognitive burden on sellers in navigating supply chain and operational complexities, 4) ensuring worldwide Amazon customers have access to the broadest selection of products from FBA sellers, and 5) driving cost efficiencies across end-to-end FBA supply chain. Unlike many companies that purchase off-the-shelf planning systems, we design and build custom solutions to suit Amazon's unique needs. Our team members are at the forefront of supply chain innovation, tackling some of the industry's most challenging problems alongside top product managers, research scientists, statisticians, economists, and software developers. We value individuals who exhibit deep technical proficiency, a desire for learning new areas, and a track record of delivering tangible results while fostering personal growth, team development, and career advancement. A day in the life As a scientist on the team, you will be involved with every aspect of the process - from idea generation, business analysis and scientific research to development and deployment of advanced models - granting you a profound sense of ownership. Your solutions have the potential to drive billions of dollars in impact for Amazon's third-party seller business. From day one, you will collaborate with experienced scientists, engineers, and product managers who are passionate about their work. Additionally, you will engage with Amazon's broader decision and research science community, enriching your perspective and mentoring fellow engineers and scientists. The ideal candidate will have the strong expertise in applying operations research methodologies to solve a wide variety of supply chain problems, involving millions of unique products, hundreds of thousands of Selling Partners, and tens of millions of customers worldwide. You will strive for simplicity and demonstrate judgment backed by mathematical rigor, as you continually seek opportunities to innovate, build, and deliver. An entrepreneurial spirit, adaptability to diverse roles, and agility in a fast-paced, high-energy, highly collaborative environment are essential. About the team Sellers are a crucial part of Amazon's ecosystem, playing an integral role in our mission to offer the Earth's largest selection and lowest prices. FBA is a service that enables third-party sellers to outsource order fulfillment to Amazon, and leverage Amazon’s world-class facilities to provide customers Prime delivery promise. By partnering with Amazon, sellers benefit from cost-effective solutions that leverage our scale and technology, gain access to Prime members worldwide, boost their sales, and free up time to focus on inventing amazing products for customers. To further ease supply chain and operational complexities for our selling partners, Amazon introduced Supply Chain by Amazon (SCA), an end-to-end, fully automated suite of supply chain services. With SCA, Amazon handles everything from picking up inventory from global manufacturing facilities, shipping across borders, managing customs clearance and ground transportation, to storing inventory in bulk, managing replenishment across Amazon and other sales channels, and delivering directly to customers— all without sellers having to worry about managing their supply chain. A pivotal service within SCA is Amazon Warehousing and Distribution, which offers best-in-class bulk storage and distribution services, ensuring sellers remain well-stocked across all their sales and fulfillment channels while reducing overall supply chain costs. The FBA team is at the heart of this operation, responsible for inventory management, automated replenishment, fulfillment, pricing, resource planning, capacity management, and a wide range of operational recommendation services for sellers. We focus on understanding seller behavior and experience, building automated tools and assistants, recommending optimal actions, designing seller policies and incentives, and developing science-driven products and services that empower third-party sellers to grow their businesses. We develop and innovate science-driven solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. Our work spans the full stack, from foundational backend systems to advanced user interfaces. Our culture is rooted in rapid prototyping, rigorous experimentation, and data-driven decision-making.
Sr. Applied Scientist, Weather Science, Prime Air
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We are seeking a highly skilled weather scientist to help invent and develop new models and strategies to support Prime Air’s drone delivery program. In this role, you will develop, build, and implement novel weather solutions using your expertise in atmospheric science, data science, and software development. You will be supported by a team of world class software engineers, systems engineers, and other scientists. Your work will drive cross-functional decision-making through your excellent oral and written communication skills, define system architecture and requirements, enable the scaling of Prime Air’s operation, and produce innovative technological breakthroughs that unlock opportunities to meet our customers' evolving demands. About the team Prime air has ambitious goals to offer its service to an increasing number of customers. Enabling a lot of concurrent flights over many different locations is central to reaching more customers. To this end, the weather team is building algorithms, tools and services for the safe and efficient operation of prime air's autonomous drone fleet.
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