Social
Conversational AI

A quick guide to Amazon’s 40-plus papers at Interspeech 2022

Speech recognition and text-to-speech predominate, but other topics include audio watermarking, automatic dubbing, and compression.

By Staff writer
September 6, 2022
6 min read
Share

Of Amazon’s more than 40 papers at this year’s Interspeech, automatic speech recognition and text-to-speech account for about half. But the others cover a range of topics, from acoustic watermarking and automatic dubbing to quantization and fairness.

Acoustic watermarking

Practical over-the-air perceptual acoustic watermarking
Ameya Agaskar

Audio classification

CNN-based audio event recognition for automated violence classification and rating for Prime Video content
Tarun Gupta, Mayank Sharma, Kenny Qiu, Xiang Hao, Raffay Hamid

Impact of acoustic event tagging on scene classification in a multi-task learning framework
Rahil Parikh, Harshavardhan Sundar, Ming Sun, Chao Wang, Spyros Matsoukas

Automatic dubbing

Isochrony-aware neural machine translation for automatic dubbing
Derek Tam, Surafel Melaku Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

Prosodic alignment for off-screen automatic dubbing
Yogesh Virkar, Marcello Federico, Robert Enyedi, Roberto Barra-Chicote

Automatic speech recognition

Compute cost amortized transformer for streaming ASR
Yi Xie, Jonathan Macoskey, Martin Radfar, Feng-Ju Chang, Brian King, Ariya Rastrow, Athanasios Mouchtaris, Grant Strimel

Cost amortized Transformer.png
"Compute cost amortized Transformer for streaming ASR" proposes a mechanism that toggles components of Transformer blocks on and off to use computational resources more efficiently.

Content-context factorized representations for automated speech recognition
David M. Chan, Shalini Ghosh

ConvRNN-T: Convolutional augmented recurrent neural network transducers for streaming speech recognition
Martin Radfar, Rohit Barnwal, Rupak Vignesh Swaminathan, Feng-Ju Chang, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris

Directed speech separation for automatic speech recognition of long-form conversational speech
Rohit Paturi, Sundararajan Srinivasan, Katrin Kirchhoff, Daniel Garcia-Romero

Domain prompts: Towards memory and compute efficient domain adaptation of ASR systems
Saket Dingliwa, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

Incremental learning for RNN-Transducer based speech recognition models
Deepak Baby, Pasquale D'Alterio, Valentin Mendelev

Knowledge distillation via module replacing for automatic speech recognition with recurrent neural network transducer
Kaiqi Zhao, Hieu Duy Nguyen, Animesh Jain, Nathan Susanj, Athanasios Mouchtaris, Lokesh Gupta, Ming Zhao

Learning to rank with BERT-based confidence models in ASR rescoring
Ting-Wei Wu, I-FAN CHEN, Ankur Gandhe

Reducing geographic disparities in automatic speech recognition via elastic weight consolidation
Viet Anh Trinh, Pegah Ghahremani, Brian King, Jasha Droppo, Andreas Stolcke, Roland Maas

RefTextLAS: Reference text biased listen, attend, and spell model for accurate reading evaluation
Phani Sankar Nidadavolu, Na Xu, Nick Jutila, Ravi Teja Gadde, Aswarth Abhilash Dara, Joseph Savold, Sapan Patel, Aaron Hoff, Veerdhawal Pande, Kevin Crews, Ankur Gandhe, Ariya Rastrow, Roland Maas

RNN-T lattice enhancement by grafting of pruned paths
Mirek Novak, Pavlos Papadopoulos

Using data augmentation and consistency regularization to improve semi-supervised speech recognition
Ashtosh Sapru

Dialogue

Contextual acoustic barge in classification for spoken dialog systems
Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki, Katrin Kirchhoff

Adversarial reweighting.png
The method presented in "Adversarial reweighting for speaker verification fairness" uses an adversarial network to identify underperforming groups in a speaker verification dataset (green) and adjusts their contribution to the training loss (bottom).

Fairness

Toward fairness in speech recognition: Discovery and mitigation of performance disparities
Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju, I-Fan Chen, Brian King, Katherine Powell, Melissa Saboowala, Karan Shetty, Andreas Stolcke

Keyword spotting

Latency control for keyword spotting
Christin Jose, Joe Wang, Grant Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis

Language identification

A multimodal strategy for singing language identification
Wo Jae Lee, Emanuele Coviello

Multidevice processing

Challenges and opportunities in multi-device speech processing
Gregory Ciccarelli, Jarred Barber, Arun Nair, Israel Cohen, Tao Zhang

Multiparty speech

Separator-transducer-segmenter: Streaming recognition and segmentation of multi-party speech
Ilya Sklyar, Anna Piunova, Christian Osendorfer

Natural-language understanding

Phonetic embedding for ASR robustness in entity resolution
Xiaozhou Zhou, Ruying Bao, William M. Campbell

Quantization

Squashed weight distribution for low bit quantization of deep models
Nikko Ström, Haidar Khan, Wael Hamza

Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition
Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta, Nathan Susanj, Athanasios Mouchtaris, Tariq Afzal, Ariya Rastrow

Sub-8-bit quantization.png
The training behavior of the algorithm proposed in "Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition", in which weights are optimized to lower quantization loss.

Signal processing

Clock skew robust acoustic echo cancellation
Karim Helwani, Erfan Soltanmohammadi, Michael M. Goodwin, Arvindh Krishnaswamy

Real-time packet loss concealment with mixed generative and predictive model
Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy

Speaker identification/verification

Adversarial reweighting for speaker verification fairness
Minho Jin, Chelsea J.-T. Ju, Zeya Chen, Yi Chieh Liu, Jasha Droppo, Andreas Stolcke

Graph-based multi-view fusion and local adaptation: Mitigating within household confusability for speaker identification
Long Chen, Yixiong Meng, Venkatesh Ravichandran, Andreas Stolcke

Graph fusion and fairness.png
The method proposed in "Graph-based multi-view fusion and local adaptation" propagates labels across a graph whose nodes represent utterances and whose weighted edges quantify the similarity between utterances.

Spoken-language understanding

Learning under label noise for robust spoken language understanding systems
Anoop Kumar, Pankaj Sharma, Aravind Illa, Sriram Venkatapathy, Subhrangshu Nandi, Pritam Varma, Anurag Dwarakanath, Aram Galstyan

On joint training with interfaces for spoken language understanding
Anirudh Raju, Milind Rao, Gautam Tiwari, Pranav Dheram, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Text-to-speech

Automatic evaluation of speaker similarity
Kamil Deja, Ariadna Sanchez, Julian Roth, Marius Cotescu

CopyCat2: A single model for multi-speaker TTS and many-to-many fine-grained prosody transfer
Sri Karlapati, Penny Karanasou, Mateusz Lajszczak, Ammar Abbas, Alexis Moinet, Peter Makarov, Ray Li, Arent van Korlaar, Simon Slangen, Thomas Drugman

Creating new voices.png
Voices created through the method presented in "Creating new voices using normalizing flows" (green) are spread across the embedding space of voices from the training set (blue), confirming that the method can generate a variety of new voices.

Creating new voices using normalizing flows
Piotr Biliński, Tom Merritt, Abdelhamid Ezzerg, Kamil Pokora, Sebastian Cygert, Kayoko Yanagisawa, Roberto Barra-Chicote, Daniel Korzekwa

Cross-lingual style transfer with conditional prior VAE and style loss
Dino Ratcliffe, You Wang, Alex Mansbridge, Penny Karanasou, Alexis Moinet, Marius Cotescu

End-to-end LPCNet: A neural vocoder with fully-differentiable LPC estimation
Krishna Subramani, Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy

Expressive, variable, and controllable duration modelling in TTS
Ammar Abbas, Tom Merritt, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Simon Slangen, Elia Gatti, Thomas Drugman

GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
Magdalena Proszewska, Grzegorz Beringer, Daniel Saez Trigueros, Tom Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote

L2-GEN: A neural phoneme paraphrasing approach to L2 speech synthesis for mispronunciation diagnosis
Daniel Zhang, Ashwinkumar Ganesan, Sarah Campbell, Daniel Korzekwa

Low data? No problem: low resource, language-agnostic conversational text-to-speech via F0- conditioned data augmentation
Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro, Adam Gabrys, Jaime Lorenzo Trueba

Mix and match: An empirical study on training corpus composition for polyglot text-to-speech (TTS)
Ziyao Zhang, Alessio Falai, Ariadna Sanchez, Orazio Angelini, Kayoko Yanagisawa

Simple and effective multi-sentence TTS with expressive and coherent prosody
Peter Makarov, Ammar Abbas, Mateusz Lajszczak, Arnaud Joly, Sri Karlapati, Alexis Moinet, Thomas Drugman, Penny Karanasou

Unify and conquer: How phonetic feature representation affects polyglot text-to-speech (TTS)
Ariadna Sanchez, Alessio Falai, Ziyao Zhang, Orazio Angelini, Kayoko Yanagisawa

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Member of Technical Staff - Applied Science , AGI Autonomy Lab
US, CA, San Francisco
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. Our work leverages large vision language models (VLMs) with reinforcement learning (RL) and world modeling to solve perception, reasoning, and planning to build useful enterprise agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. Key job responsibilities You will contribute directly to AI agent development in an applied research role to improve the multi-model perception and visual-reasoning abilities of our agent. Daily responsibilities including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.
Data Scientist, Digital Acceleration
IN, TN, Chennai
Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action? The Amazon Digital Acceleration Analytics team is looking for an analytical and technically skilled individual to join our team. In this role, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission This role offers wide scope, autonomy, and ownership. You will work closely with software engineers & data engineers to put algorithms into practice. You should have strong business judgement, excellent written and verbal communication skills. The candidate should be willing to take on challenging initiatives and be capable of working both independently and with others as a team. Key job responsibilities We are looking for an experienced data scientist with strong foundations in mathematics, statistics & machine learning with exceptional communication and leadership skills, and a proven track record of delivery. In this role, You will Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for engineering teams. Design and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. Drive end-to-end statistical analysis that have a high degree of ambiguity, scale, and complexity. Research and develop advanced Generative AI based solutions to solve diverse customer problems. About the team The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.
Applied Scientist, AI Automation, Prime Video
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! We are forming a new organization within Prime Video to redefine our operational landscape through the power of artificial intelligence. As a Applied Scientist within this initiative, you will be a technical leader helping to design and build the intelligent systems that power our vision. You will tackle complex and ambiguous problems, designing and delivering scalable and resilient agentic AI and ML solutions from the ground up. You will not only write high-quality, maintainable software and models, but also mentor other scientists, influence our technical strategy, and drive engineering best practices across the team. Your work will directly contribute to making Prime Video's operations more efficient and will set the technical foundation for years to come. We're seeking candidates with strong experience in computer vision and generative AI technologies. In this role, you'll apply cutting-edge techniques in image and video understanding, visual content generation, and multimodal AI systems to transform how Prime Video operates at scale. Key job responsibilities • Lead the design and architecture of highly scalable, available, and resilient services for our AI automation platform. • Write high-quality, maintainable, and robust code to solve complex business problems, building flexible systems without over-engineering. • Act as a technical leader and mentor for other engineers on the team, assisting with career growth and encouraging excellence. • Work through ambiguous requirements, cut through complexity, and translate business needs into scalable technical solutions. • Take ownership of the full software development lifecycle, including design, testing, deployment, and operations. • Work closely with product managers, scientists, and other engineers to build and launch new features and systems. About the team This role offers a unique opportunity to shape the future of one of Amazon's most exciting businesses through the application of AI technologies. If you're passionate about leveraging AI to drive real-world impact at massive scale, we want to hear from you.
Applied Scientist, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Applied Scientist, Frontier AI & Robotics (FAR)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Applied Scientist II, US Prime and Marketing Tech
US, WA, Seattle
Are you excited to help customers discover the hottest and best reviewed products? The Discovery Tech team helps customers discover and engage with new, popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside advanced tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon’s cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon’s customers all over the globe. We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation. As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. To know more about Amazon science, please visit https://www.amazon.science
Senior Applied Scientist, US Prime & Marketing Technology (UPMT)
ES, B, Barcelona
Are you a scientist passionate about advancing the frontiers of computer vision, machine learning, or large language models? Do you want to work on innovative research projects that lead to innovative products and scientific publications? Would you value access to extensive datasets? If you answer yes to any of these questions, you'll find a great fit at Amazon. We're seeking a hands-on researcher eager to derive, implement, and test the next generation of Generative AI, computer vision, ML, and NLP algorithms. Our research is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish pioneering research that pushes the boundaries of what's possible. To achieve our vision, we think big and tackle complex technological challenges at the forefront of our field. Where technology doesn't exist, we create it. Where it does, we adapt it to function at Amazon's scale. We need team members who are passionate, curious, and willing to learn continuously. Key job responsibilities * Derive novel computer vision and ML models or LLMs/VLMs. * Design and develop scalable ML models. * Create and work with large datasets * Work with large GPU clusters. * Work closely with software engineering teams to deploy your innovations. * Publish your work at major conferences/journals. * Mentor team members in the use of your AI models. A day in the life As a Senior Applied Scientist at Amazon, your typical day might look like this: * Dive into coding, deriving new ML models for computer vision or NLP * Experiment with massive datasets on our GPU clusters * Brainstorm with your team to solve complex AI challenges * Collaborate with engineers to turn your research into real products * Write up your findings for publication in top journals or conferences * Mentor junior team members on AI concepts and implementation About the team DiscoVision, a science unit within Amazon's UPMT, focuses on advancing visual content capabilities through state-of-the-art AI technology. Our team specializes in developing state-of-the-art technologies in text-to-image/video Generative AI, 3D modeling, and multimodal Large Language Models (LLMs).
Principal Applied Scientist, Automated Reasoning
US, WA, Seattle
We are seeking a Principal Applied Scientist to lead research and development in automated reasoning, formal verification, and program analysis. You will drive innovation in making formal methods practical and accessible for real-world systems at cloud scale. Key job responsibilities - Lead research initiatives in automated reasoning, formal verification, SMT solving, model checking, or program analysis - Design and implement novel algorithms and techniques that advance the state of the art - Mentor and guide applied scientists, research scientists, and engineers - Collaborate with product teams to transition research into production systems - Define technical vision and strategy for automated reasoning initiatives - Represent AWS in the academic and research community - Drive cross-organizational impact through technical leadership About the team The Automated Reasoning Group at AWS develops and applies cutting-edge formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.
Applied Scientist II, Amazon Business Companion
IN, TS, Hyderabad
Do you want to join an innovative team of scientists who leverage machine learning and statistical techniques to revolutionize how businesses discover and purchase products on Amazon? Are you passionate about building intelligent systems that understand and predict complex B2B customer needs? The Amazon Business team is looking for exceptional Applied Science to help shape the future of B2B commerce. Amazon Business is one of Amazon's fastest-growing initiatives focused on serving business customers, from individual professionals to large institutions, with unique and complex purchasing needs. Our customers require sophisticated solutions that go beyond traditional B2C experiences, including bulk purchasing, approval workflows, and business-grade service support. The AB-MSET Applied Science team focuses on building intelligent systems for delivering personalized, contextual service experiences throughout the customer lifecycle. We apply advanced machine learning techniques to develop sophisticated intent detection models for business customer service needs, create intelligent matching algorithms for optimal service routing based on multiple variables including customer value, maturity, effort, and issue complexity, build predictive models to enable proactive service interventions, design recommendation systems for self-service solutions, and develop ML models for automated service resolution. As an Applied Scientist on the team, you will design and develop state-of-the-art ML models for service intent classification, routing optimization, and customer experience personalization. You will analyze large-scale business customer interaction data to identify patterns and opportunities for automation, create scalable solutions for complex B2B service scenarios using advanced ML techniques, and work closely with engineering teams to implement and deploy models in production. You will collaborate with business stakeholders to identify opportunities for ML applications, establish automated processes for model development, validation, and maintenance, lead research initiatives to advance the state-of-the-art in B2B service science, and mentor other scientists and engineers in applying ML techniques to business problems.
Data Scientist, Amazon Leo Global Planning, Amazon Leo
US, WA, Bellevue
Amazon Leo is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. Do you get excited by aerospace, space exploration, and/or satellites? Do you want to help build solutions at Amazon Leo to transform the space industry? If so, then we would love to talk! Key job responsibilities Work cross-functionally with product, business development, and various technical teams (engineering, science, simulations, etc.) to execute on the long-term vision, strategy, and architecture for the science-based global demand forecast. Design and deliver modern, flexible, scalable solutions to integrate data from a variety of sources and systems (both internal and external) and develop Bandwidth Usage models at granular temporal and geographic grains, deployable to Leo traffic management systems. Work closely with the capacity planning science team to ensure that demand forecasts feed seamlessly into their systems to deliver continuous optimization of resources. Lead short and long terms technical roadmap definition efforts to deliver solutions that meet business needs in pre-launch, early-launch, and mature business phases. Synthesize and communicate insights and recommendations to audiences of varying levels of technical sophistication to drive change across Amazon Leo. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the team The Amazon Leo Global Demand Planning team's mission is to map customer demand across space and time. We enable Amazon Leo's long-term success by delivering actionable insights and scientific forecasts across geographies and customer segments to empower long range planning, capacity simulations, business strategy, and hardware manufacturing recommendations through scalable tools and durable mechanisms.
See more jobs
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 | © 1996-2026 Amazon.com, Inc. or its affiliates

Social