Subscribe
Follow Us
Conversational AI

A quick guide to Amazon's papers at Interspeech 2023

Speech recognition predominates, but Amazon's research takes in data representation, dialogue management, question answering, and more.

By Staff writer
August 18, 2023
Share

Amazon's papers at Interspeech 2023, sorted by research topic.

Automatic speech recognition

A metric-driven approach to conformer layer pruning for efficient ASR inference
Dhanush Bekal, Karthik Gopalakrishnan, Karel Mundnich, Srikanth Ronanki, Sravan Bodapati, Katrin Kirchhoff

Conmer: Streaming Conformer without self-attention for interactive voice assistants
Martin Radfar, Paulina Lyskawa, Brandon Trujillo, Yi Xie, Kai Zhen, Jahn Heymann, Denis Filimonov, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris

DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer
Goeric Huybrechts, Srikanth Ronanki, Xilai Li, Hadis Nosrati, Sravan Bodapati, Katrin Kirchhoff

Distillation strategies for discriminative speech recognition rescoring
Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yi Gu, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Effective training of attention-based contextual biasing adapters with synthetic audio for personalised ASR
Burin Naowarat, Philip Harding, Pasquale D'Alterio, Sibo Tong, Bashar Awwad Shiekh Hasan

Human transcription quality improvement
Jian Gao, Hanbo Sun, Cheng Cao, Zheng Du

Human transcription quality.png
In “Human transcription quality improvement”, Amazon researchers use machine learning models to align and score multiple transcription hypotheses from crowd workers.

Learning when to trust which teacher for weakly supervised ASR
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath (Nath) Chennupati, Andreas Stolcke

Model-internal slot-triggered biasing for domain expansion in neural transducer ASR models
Edie Lu, Philip Harding, Kanthashree Mysore Sathyendra, Sibo Tong, Xuandi Fu, Jing Liu, Feng-Ju (Claire) Chang, Simon Wiesler, Grant Strimel

Multi-view frequency-attention alternative to CNN frontends for automatic speech recognition
Belen Alastruey Lasheras, Lukas Drude, Jahn Heymann, Simon Wiesler

Multilingual contextual adapters to improve custom word recognition in low-resource languages
Devang Kulshreshtha, Saket Dingliwal, Brady Houston, Sravan Bodapati

Multilingual contextual adapters.png
“Multilingual contextual adapters to improve custom word recognition in low-resource languages” proposes a three-stage process for training multilingual contextual adapters. Stage I trains a multilingual encoder; stage II learns multilingual contextual adapters by freezing the encoder; and stage III jointly optimizes both components on the target language.

PATCorrect: Non-autoregressive phoneme-augmented transformer for ASR error correction
Ziji Zhang, Zhehui Wang, Raj Kamma, Sharanya Eswaran, Narayanan Sadagopan

Personalization for BERT-based discriminative speech recognition rescoring
Jari Kolehmainen, Yi Gu, Aditya Gourav, Prashanth Gurunath Shivakumar, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Personalized predictive ASR for latency reduction in voice assistants
Andreas Schwarz, Di He, Maarten Van Segbroeck, Mohammed Hethnawi, Ariya Rastrow

Record deduplication for entity distribution modeling in ASR transcripts
Tianyu Huang, Chung Hoon Hong, Carl Wivagg, Kanna Shimizu

Scaling laws for discriminative speech recognition rescoring models
Yi Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Selective biasing with trie-based contextual adapters for personalised speech recognition using neural transducers
Philip Harding, Sibo Tong, Simon Wiesler

Streaming speech-to-confusion network speech recognition
Denis Filimonov, Prabhat Pandey, Ariya Rastrow, Ankur Gandhe, Andreas Stolcke

Data representation

Don’t stop self-supervision: Accent adaptation of speech representations via residual adapters
Anshu Bhatia, Sanchit Sinha, Saket Dingliwal, Karthik Gopalakrishnan, Sravan Bodapati, Katrin Kirchhoff

Dialogue management

Parameter-efficient low-resource dialogue state tracking by prompt tuning
Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, Tagyoung Chung, Violet Peng

Parameter efficient low resource dialogue state tracking.png
“Parameter-efficient low-resource dialogue state tracking by prompt tuning” proposes a method for using language-model prompts to do dialogue state tracking, with a separate, fixed-length embedding for each input segment.

Grapheme-to-phoneme conversion

Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings
Sam Ribeiro, Giulia Comini, Jaime Lorenzo Trueba

Keyword spotting

On-device constrained self-supervised speech representation learning for keyword spotting via knowledge distillation
Gene-Ping Yang, Yue Gu, Qingming Tang, Dongsu Du, Yuzong Liu

Natural-language understanding

Quantization-aware and tensor-compressed training of transformers for natural language understanding
Zi Yang, Samridhi Choudhary, Siegfried Kunzmann, Zheng Zhang

Sampling bias in NLU models: Impact and mitigation
Zefei Li, Anil Ramakrishna, Anna Rumshisky, Andy Rosenbaum, Saleh Soltan, Rahul Gupta

Understanding disrupted sentences using underspecified abstract meaning representation
Angus Addlesee, Marco Damonte

Paralinguistics

Towards paralinguistic-only speech representations for end-to-end speech emotion recognition
George Ioannides, Michael Owen, Andrew Fletcher, Viktor Rozgic, Chao Wang

Utility-preserving privacy-enabled Speech embeddings for emotion detection
Chandrashekhar Lavania, Sanjiv Das, Xin Huang, Kyu Han

Question answering

Question content alignment.png
In “Question-context alignment and answer-context dependencies for effective answer sentence selection,” Amazon researchers propose a method that uses the sentences surrounding answer candidates as additional context. Given probability distributions over sequences of words, the method aligns questions with answer candidates and context by using optimal transport to move probability from one distribution to another.

Question-context alignment and answer-context dependencies for effective answer sentence selection
Minh Van Nguyen, Kishan K C, Toan Nguyen, Thien Nguyen, Ankit Chadha, Thuy Vu

Speaker diarization

Lexical speaker error correction: Leveraging language models for speaker diarization error correction
Rohit Paturi, Sundararajan Srinivasan, Xiang Li

Speech translation

Knowledge distillation on joint task end-to-end speech translation

Khandokar Md. Nayem, Ran Xue, Ching-Yun (Frannie) Chang, Akshaya Vishnu Kudlu Shanbhogue

Text-to-speech

Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Guangyang Zhang, Tom Merritt, Sam Ribeiro, Biel Tura Vecino, Kayoko Yanagisawa, Kamil Pokora, Abdelhamid Ezzerg, Sebastian Cygert, Ammar Abbas, Piotr Bilinski, Roberto Barra-Chicote, Daniel Korzekwa, Jaime Lorenzo Trueba

Cross-lingual prosody transfer for expressive machine dubbing
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski, Patrick Tobing, Ravi chander Vipperla, Vincent Pollet

Diffusion-based accent modelling in speech synthesis
Kamil Deja, Georgi Tinchev, Marta Czarnowska, Marius Cotescu, Jasha Droppo

eCat: An end-to-end model for multi-speaker TTS & many-to-many fine-grained prosody transfer
Ammar Abbas, Sri Karlapati, Bastian Schnell, Penny Karanasou, Marcel Granero Moya, Amith Nagaraj, Ayman Boustati, Nicole Peinelt, Alexis Moinet, Thomas Drugman

Expressive machine dubbing through phrase-level cross-lingual prosody transfer
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski, Giuseppe Coccia, Patrick Tobing, Ravi chander Vipperla, Viacheslav Klimkov, Vincent Pollet

Expressive machine dubbing.png
The architecture proposed in “Expressive machine dubbing through phrase-level cross-lingual prosody transfer” relies on a reference encoder that explicitly models noise.

Multilingual context-based pronunciation learning for text-to-speech
Giulia Comini, Sam Ribeiro, Fan Yang, Heereen Shim, Jaime Lorenzo Trueba

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Cientista de Dados - Vaga afirmativa para mulheres, AWS Gen AI Innovation Center
BR, SP, Sao Paulo
A Amazon lançou o Centro de Inovação de IA Generativa em junho de 2023 para ajudar os clientes da AWS a acelerar a inovação e o sucesso empresarial com IA Generativa (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai -centro de inovação). Este Centro de Inovação oferece oportunidades para inovar em uma organização de ritmo acelerado que contribui para projetos e tecnologias revolucionárias que são implantadas em dispositivos e na nuvem. Como cientista de dados, você é proficiente em projetar e desenvolver soluções avançadas baseadas em IA generativa para resolver diversos problemas dos clientes. Você trabalhará com terabytes de texto, imagens e outros tipos de dados para resolver problemas do mundo real por meio da Gen AI. Você trabalhará em estreita colaboração com equipes de contas e estrategistas de ML para definir o caso de uso, e com outros cientistas e engenheiros de ML da equipe para projetar experimentos e encontrar novas maneiras de agregar valor ao cliente. A pessoa selecionado possuirá habilidades técnicas e de contato com o cliente que permitirão que você faça parte da equipe técnica da AWS no ecossistema/ambiente de nossos provedores de soluções, bem como diretamente para os clientes finais. Você será capaz de conduzir discussões com pessoal técnico e de gerenciamento sênior de clientes e parceiros. A day in the life Aqui na AWS, abraçamos nossas diferenças. Estamos empenhados em promover a nossa cultura de inclusão. Temos dez grupos de afinidade liderados por funcionários, alcançando 40.000 funcionários em mais de 190 filiais em todo o mundo. Temos ofertas de benefícios inovadoras e organizamos experiências de aprendizagem anuais e contínuas, incluindo nossas conferências Conversations on Race and Ethnicity (CORE) e AmazeCon (diversidade de gênero). A cultura de inclusão da Amazon é reforçada pelos nossos 16 Princípios de Liderança, que lembram os membros da equipe de buscar perspectivas diversas, aprender e ser curiosos e ganhar confiança. About the team Equilíbrio trabalho/vida pessoal Nossa equipe valoriza muito o equilíbrio entre vida pessoal e profissional. Não se trata de quantas horas você passa em casa ou no trabalho; trata-se do fluxo que você estabelece que traz energia para ambas as partes da sua vida. Acreditamos que encontrar o equilíbrio certo entre sua vida pessoal e profissional é fundamental para a felicidade e a realização ao longo da vida. Oferecemos flexibilidade no horário de trabalho e incentivamos você a encontrar seu próprio equilíbrio entre trabalho e vida pessoal. Mentoria e crescimento de carreira Nossa equipe se dedica a apoiar novos membros. Temos uma ampla combinação de níveis de experiência e mandatos e estamos construindo um ambiente que celebra o compartilhamento de conhecimento e a orientação. Nossos membros seniores desfrutam de orientação individual e revisões de código completas, mas gentis. Nós nos preocupamos com o crescimento de sua carreira e nos esforçamos para atribuir projetos com base no que ajudará cada membro da equipe a se tornar um engenheiro mais completo e capacitá-los a assumir tarefas mais complexas no futuro. We are open to hiring candidates to work out of one of the following locations: Sao Paulo, SP, BRA
Applied Scientist, Outbound Communications, Traffic and Marketing Tech
US, WA, Seattle
Outbound Communications own the worldwide charter for delighting our customers with timely, relevant notifications (email, mobile, SMS and other channels) to drive awareness and discovery of Amazon’s products and services. We meet customers at their channel of preference with the most relevant content at the right time and frequency. We directly create and operate marketing campaigns, and we have also enabled select partner teams to build programs by reusing and extending our infrastructure. We optimize for customers to receive the most relevant and engaging content across all of Amazon worldwide, and apply the appropriate guardrails to ensure a consistent and high-quality CX. Outbound Communications seek a talented Applied Scientist to join our team to develop the next generation of automated and personalized marketing programs to help Amazon customers in their shopping journeys worldwide. Come join us in our mission today! Key job responsibilities As an Applied Scientist on the team, you will lead the roadmap and strategy for applying science to solve customer problems in the automated marketing domain. This is an opportunity to come in on Day 0 and lead the science strategy of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive messaging experiences. You will be part of a multidisciplinary team and play an active role in translating business and functional requirements into concrete deliverables. You will work closely with product management and the software development team to put solutions into production. You will apply your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. You will have a unique opportunity to produce and deliver models that help build best-in-class customer experiences and build systems that allow us to deploy these models to production with low latency and high throughput. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Applied Scientist, Artificial General Intelligence
US, WA, Seattle
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (Gen AI) in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and Gen AI in Computer Vision, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
Senior Applied Scientist, Fulfillment by Amazon
US, WA, Bellevue
The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Senior Applied Scientist, with expertise in machine learning and a proven record of solving business problems through scalable ML solutions, to join our top-notch cross-domain FBA science team. We want to learn seller behaviors, understand seller experience, build automated LLM-based solutions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. We also predict potentially costly defects that may occur during packing, shipping, receiving and storing the inventory. We aim to prevent such defects before occurring while we are also fulfilling customer demand as quickly and efficiently as possible, in addition to managing returns and reimbursements. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. As a senior applied scientist, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, and reinforcement learning. This role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate scientific work into production systems. Key job responsibilities - As a senior member of the science team, you will play an integral part in building Amazon's FBA management system. - Research and develop machine learning models to solve diverse business problems faced in Seller inventory management systems. - Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for research and applied scientists, as well as engineering and product teams. - Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. - Review and audit modeling processes and results for other scientists, both junior and senior. - Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. Your solutions may lead to billions of dollars impact on either the topline or the bottom line of Amazon third-party seller business. As a senior scientist on the team, you will be involved in every aspect of the process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. You are expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by mathematical proof. You will also collaborate with the broader decision and research science community in Amazon to broaden the horizon of your work and mentor engineers and scientists. The successful candidate will have the strong expertise in applying machine learning models in an applied environment and is looking for her/his next opportunity to innovate, build, deliver, and impress. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. The candidate will need to be entrepreneurial, wear many hats, and work in a fast-paced, high-energy, highly collaborative environment. 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 team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
Applied Scientist, FBA Science
US, WA, Bellevue
The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Applied Scientist, with expertise and experience in machine learning, to join our top-notch cross-domain FBA science team. We want to learn seller behaviors, understand seller experience, build automated LLM-based solutions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. We also predict potentially costly defects that may occur during packing, shipping, receiving and storing the inventory. We aim to prevent such defects before occurring while we are also fulfilling customer demand as quickly and efficiently as possible, in addition to managing returns and reimbursements. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. As an applied scientist, you will design and implement ML solutions that will likely draw from a range of scientific areas such as supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, and reinforcement learning. This role has high visibility to senior Amazon business leaders and involves working with other senior and principal scientists, and partnering with engineering and product teams to integrate scientific work into production systems. Key job responsibilities - Research and develop machine learning models to solve diverse FBA business problems. - Translate business requirements/problems into specific plans for research and applied scientists, as well as engineering and product teams. - Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. - Work closely with teams of scientists, product managers, program managers, software engineers to drive production model implementations. - Build scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers A day in the life In this role, you will work in machine learning with significant scope, impact, and high visibility. Your solutions may lead to billions of dollars impact on either the topline or the bottom line of Amazon third-party seller business. As an applied scientist, you will be involved in every aspect of the scientific development process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. You are expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by mathematical proof. You will also collaborate with the broader decision and research science community in Amazon to broaden the horizon of your work and mentor engineers and scientists. The successful candidate will have the strong expertise in applying machine learning models in an applied environment and is looking for her/his next opportunity to innovate, build, deliver, and impress. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. 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 team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
Economist, Economic Decision Science
GB, London
Economic Decision Science is a central science team working across a variety of topics in the EU Stores business and beyond. We work closely EU business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with EU- and US-based interdisciplinary teams. We are looking for a Senior Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations and analytics teams to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities - Provide data-driven guidance and recommendations on strategic questions facing the EU Retail leadership - Scope, design and implement version-zero (V0) models and experiments to kickstart new initiatives, thinking, and drive system-level changes across Amazon - Build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challenges - Influence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change We are open to hiring candidates to work out of one of the following locations: London, GBR
Applied Scientist, AWS Transcribe
US, WA, Seattle
Amazon is looking for a passionate, talented, and inventive Applied Scientist with background in Natural Language Processing (NLP), Deep Learning, Generative AI (GenAI) to help build industry-leading technology in contact center. The ideal candidate should have a robust foundation in NLP and machine learning and a keen interest in advancing the field. The ideal candidate would also enjoy operating in dynamic environments, have the self-motivation to take on challenging problems to deliver big customer impact, and move fast to ship solutions and innovate along the development process. As part of our Transcribe science team in Amazon AWS AI, you will have the opportunity to build the next generation call center analytic solutions. You will work along side a supportive and collaborative team with a healthy mix of scientists, software engineers and language engineers to research and develop state-of-the-art technology for natural language processing. A day in the life AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA
Senior Applied Scientist, CreST
US, WA, Seattle
We are looking for an Applied Scientist to join our Seattle team. As an Applied Scientist, you are able to use a range of science methodologies to solve challenging business problems when the solution is unclear. Our team solves a broad range of problems ranging from natural knowledge understanding of third-party shoppable content, product and content recommendation to social media influencers and their audiences, determining optimal compensation for creators, and mitigating fraud. We generate deep semantic understanding of the photos, and videos in shoppable content created by our creators for efficient processing and appropriate placements for the best customer experience. For example, you may lead the development of reinforcement learning models such as MAB to rank content/product to be shown to influencers. To achieve this, a deep understanding of the quality and relevance of content must be established through ML models that provide those contexts for ranking. In order to be successful in our team, you need a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as SageMaker, S3, and EC2 with a variety of skillset in shallow and deep learning ML models, particularly in NLP and CV. You will bring knowledge in many of these domains along with your own specialties. Key job responsibilities • Use statistical and machine learning techniques to create scalable and lasting systems. • Analyze and understand large amounts of Amazon’s historical business data for Recommender/Matching algorithms • Design, develop and evaluate highly innovative models for NLP. • Work closely with teams of scientists and software engineers to drive real-time model implementations and new feature creations. • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation. • Research and implement novel machine learning and statistical approaches, including NLP and Computer Vision A day in the life In this role, you’ll be utilizing your NLP or CV skills, and creative and critical problem-solving skills to drive new projects from ideation to implementation. Your science expertise will be leveraged to research and deliver often novel solutions to existing problems, explore emerging problems spaces, and create or organize knowledge around them. About the team Our team puts a high value on your work and personal life happiness. 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 you. 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 establish your own harmony between your work and personal life. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Seattle, WA, USA
Applied Scientist, Automated Reasoning Group
US, WA, Seattle
The Automated Reasoning Group in AWS Platform is looking for an Applied Scientist with experience in building scalable solver solutions that delight customers. You will be part of a world-class team building the next generation of automated reasoning tools and services. AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. You will apply your knowledge to propose solutions, create software prototypes, and move prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You will use your strong verbal and written communication skills, are self-driven and own the delivery of high quality results in a fast-paced environment. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. See https://aws.amazon.com/security/provable-security/ As an Applied Scientist in AWS Platform, you will play a pivotal role in shaping the definition, vision, design, roadmap and development of product features from beginning to end. You will: - Define and implement new solver applications that are scalable and efficient approaches to difficult problems - Apply software engineering best practices to ensure a high standard of quality for all team deliverables - Work in an agile, startup-like development environment, where you are always working on the most important stuff - Deliver high-quality scientific artifacts - Work with the team to define new interfaces that lower the barrier of adoption for automated reasoning solvers - Work with the team to help drive business decisions The AWS Platform is the glue that holds the AWS ecosystem together. From identity features such as access management and sign on, cryptography, console, builder & developer tools, to projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second. Learn and Be Curious. We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members. Inclusion and Diversity. Our team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more. Key job responsibilities Work closely with internal and external users on defining and extending application domains. Tune solver performance for application-specific demands. Identify new opportunities for solver deployment. About the team Solver science is a talented team of scientists from around the world. Expertise areas include solver theory, performance, implementation, and applications. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Portland, OR, USA | Seattle, WA, USA
Applied Scientist*, Japan Store Tech
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our monthly newsletter
Amazon.com | Conditions of Use | Privacy | © 1996-2024 Amazon.com, Inc. or its affiliates
Follow Us