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  • The SOCKEYE neural machine translation toolkit at AMTA 2018
    Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post
    AMTA 2018
    2018
    We describe SOCKEYE, 1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural
    Machine learning
  • Multichannel audio front-end for far-field automatic speech recognition
    Amit S. Chhetri, Philip Hilmes, Trausti Kristjansson, Robert Ayrapetian, Wai Chu, Mohamed Mansour, Xiaoxue Li, Xianxian Zhang
    EUSIPCO 2018
    2018
    Far-field automatic speech recognition (ASR) is a key enabling technology that allows untethered and natural voice interaction between users and Amazon Echo family of products. A key component in realizing far-field ASR on these products is the suite of audio front-end (AFE) algorithms that helps in mitigating acoustic environmental challenges and thereby improving the ASR performance. In this paper, we
    Conversational AI
  • CogCompNLP: Your Swiss army knife for NLP
    Daniel Khashabi, Mark Sammons, Christos Christodoulopoulos, Bhargav Mangipudi, Tom Redman, Ben Zhou, Guanheng Luo, Shaoshi Ling, Dan Roth
    LREC 2018
    2018
    Implementing a Natural Language Processing (NLP) system requires considerable engineering effort: creating data-structures to represent language constructs; reading corpora annotations into these data-structures; applying off-the-shelf NLP tools to augment the text representation; extracting features and training machine learning components; conducting experiments and computing performance statistics; and
    Conversational AI
  • Advancing the state of the art in open domain dialog systems through the Alexa Prize
    Chandra Khatri, Behnam Hedayatnia, Chandra Khatri, Anushree Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan
    Alexa Prize SocialBot Grand Challenge 2 Proceedings
    2018
    Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models,
    Related: Innovations from the 2018 Alexa Prize
    Machine learning
  • Context encoding for semantic segmentation
    Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal
    CVPR 2018
    2018
    Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic
    Computer vision
  • Online Embedding Compression for Text Classification using Low Rank Matrix Factorization
    Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit S. Dhillon
    AAAI 2019
    2018
    Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training, to compress the word embedding layer which represents
    Related: New method for compressing neural networks better preserves accuracy
    Conversational AI
  • Alexa for Business
    Daniel Podlogar, Jacob Peddicord, Eric ChenMou, Victor Rojo, Rob Pittfield
    2018
    This repository holds short helper code samples, that demonstrate how to achieve certain functionality with enterprise Alexa skills, and in particular with Alexa for Business. Some samples are more complete, such as the Help Desk skill, but others will focus on specific components of a use case or integration.
    Conversational AI
  • Quivela2 protocol verifier
    Daniel Schoepe, Sean McLaughlin, Richard Cook, James Siri, Henri Yandell
    2018
    Quivela2 is a tool to verify protocols modeled as object-oriented programs. Quivela is a prototype tool for constructing proofs of the security of cryptographic protocols.
    Automated reasoning
  • Alexa Auto SDK
    Muni Sakkuru, Mark Wharton, Shotaro Uchida
    2018
    The Alexa Auto SDK contains essential client-side software required to integrate Alexa into the automobile. The Auto SDK provides libraries that connect to Alexa and expose interfaces for your vehicle software to implement the platform-specific behavior for audio input, media streaming, calling through a connected phone, turn-by-turn navigation, controlling vehicle features such as heaters and lights, and
    Conversational AI
  • Alexa APIs for Java
    Jonathan Breedlove, James Siri, Nikhil Yogendra Murali, Olivia Sung, Mario Doiron
    2018
    The Alexa APIs for Java consists of Java POJOS that represent the request and response JSON of Alexa services. These models act as a core dependency for the Alexa Skills Kit Java SDK These model classes are auto-generated using the JSON schemas in the developer documentation.
    Conversational AI
  • FEVER: Fact Extraction and VERification
    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
    2018
    In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as SUPPORTED, REFUTED or NOTENOUGHINFO by annotators achieving 0.6841
    Conversational AI
  • Alexa APIs for Python
    Nikhil Yogendra Murali, Shreyas Govinda Raju, Jacob Peddicord, Mario Doiron, Kaiming Tao
    2018
    The Alexa APIs for Python consists of python classes that represent the request and response JSON of Alexa services. These models act as a core dependency for the Alexa Skills Kit Python SDK. These model classes are auto-generated using the JSON schemas in the developer documentation.
    Conversational AI
  • Handwritten text recognition (OCR) with MXNet Gluon
    Thomas Delteil, Jonathan Chung
    2018
    This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.
    Cloud and systems
  • Firectl
    Noah Meyerhans, Samuel Karp, Austin Vazquez, Xibin Gao, Kern Walster, Arun Gupta, Michael Coulter, Stanislas Lange, Bobby Gammill , Yasin Turan, Volker Simonis, Henry Wang, Nikita Mochalov, Luminita Voicu, Kazuyoshi Kato, Cody Roseborough, Boris Popovschi, Antonio Ojea
    2018
    Firectl is a basic command-line tool that lets you run arbitrary Firecracker MicroVMs via the command line. This lets you run a fully functional Firecracker MicroVM, including console access, read/write access to filesystems, and network connectivity.
    Cloud and systems
  • A basic Go interface to the Firecracker API
    Kazuyoshi Kato, Xibin Gao, Samuel Karp, Noah Meyerhans, Erik Sipsma, Austin Vazquez, Maksym Pavlenko, Jerome Gravel-Niquet, David Son
    2018
    This package is a Go library to interact with the Firecracker API. It is designed as an abstraction of the OpenAPI-generated client that allows for convenient manipulation of Firecracker VM from Go programs. There are some Firecracker features that are not yet supported by the SDK. These are tracked as GitHub issues with the firecracker-feature label. Contributions to address missing features are welcomed
    Cloud and systems
  • Firecracker presentation website
    Radu Weiss, Raj Bennin, Takahiro Itazuri, Will Stewart, Edouard Bonlieu, Nikita Sobolev, Alexandra Iordache, Christopher Mayfield, Romaric Philogène, Alberto P. Martí
    2018
    This is the presentation website for Firecracker. We take pull request for content and FAQ improvements, as well as additons to the list of Firecracker integrations. When contributing to HTML pages in this repo, please format the entire file with the latest stable Prettier release, using the settings below for the HTML parser.
    Cloud and systems
  • Visual Search with Amazon SageMaker and AWS DeepLens
    Brent Rabowsky
    2018
    This repository provides resources for implementing a visual search engine. Visual search is the central component of an interface where instead of asking for something by voice or text, you show what you are looking for. When shown a real world, physical item, an AWS DeepLens device generates a feature vector representing that item. The feature vector generated by the AWS DeepLens device is sent to the
    Search and information retrieval
  • SPEKE reference server
    Jim Thario, Vinay Calastry, Jacob Peddicord, Yufei Gao, Jared Stewart, Shinya Kawaguchi, Ritchie Robershaw, Raees Iqbal, Tomohiro Matsuzawa
    2018
    Secure Packager and Encoder Key Exchange (SPEKE) is part of the AWS Elemental content encryption protection strategy for media services customers. SPEKE defines the standard for communication between AWS Media Services and digital rights management (DRM) system key servers. SPEKE is used to supply keys to encrypt video on demand (VOD) content through AWS Elemental MediaConvert and for live content through
    Cloud and systems
  • FEVER (2018)
    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
    2018
    FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.
    Conversational AI
  • Machine translation accelerates how Alexa learns new languages
    Penny Karanasou
    May 29, 2018
    Amazon Science Promo Image (Squid)
    As Alexa-enabled devices continue to expand into new countries, we propose an approach for quickly bootstrapping machine-learning models in new languages, with the aim of more efficiently bringing Alexa to new customers around the world.
    Conversational AI
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Member of Technical Staff, AGI Autonomy
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. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). 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. We’d love for you to join our lab and build it from the ground up! Key job responsibilities You will contribute directly to AI agent development in an applied research role, including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.
Research Scientist, Artificial General Intelligence - Data Services
US, MA, Boston
AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
Research Scientist, Artificial General Intelligence - Data Services
US, MA, Boston
AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
Applied Scientist (Contract), Artificial General Intelligence
US, WA, Bellevue
This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
Applied Scientist, Off-Search Relevance, Sponsored Products
CA, ON, Toronto
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
Applied Scientist II, Amazon Industrial Robotics
US, MA, N.reading
Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement methods for dexterous manipulation - Design and implement methods for use of dexterous end effectors with force and tactile sensing - Develop a hierarchical system that combines low-level control with high-level planning - Utilize state-of-the-art manipulation models and optimal control techniques
Applied Scientist II, AWS Just-Walk-Out Science Team
US, WA, Seattle
As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. The Team Just Walk Out (JWO) is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go! Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design. Key job responsibilities Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. As an Applied Scientist, you will help solve a variety of technical challenges and mentor other scientists. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people. A key focus of this role will be developing and implementing advanced visual reasoning systems that can understand complex spatial relationships and object interactions in real-time. You'll work on designing autonomous AI agents that can make intelligent decisions based on visual inputs, understand customer behavior patterns, and adapt to dynamic retail environments. This includes developing systems that can perform complex scene understanding, reason about object permanence, and predict customer intentions through visual cues. About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
Member of Technical Staff, Technical Product Manager, AGI Autonomy
US, MA, Boston
We're a new research lab based in San Francisco and Boston focused on developing foundational capabilities for useful AI agents. We're pursuing several key research bets that will enable AI agents to perform real-world actions, learn from human feedback, self-course-correct, and infer human goals. We're particularly excited about combining large language models (LLMs) with reinforcement learning (RL) to solve reasoning and planning, learned world models, and generalizing agents to physical environments. We're a small, talent-dense team with the resources and scale of Amazon. Each team has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. AI agents are the next frontier—the right research bets can reinvent what's possible. Join us and help build this lab from the ground up. Key job responsibilities * Define the product vision and roadmap for our agentic developer platform, translating research into products developers love * Partner deeply with research and engineering to identify which capabilities are ready for productization and shape how they're exposed to customers * Own the developer experience end-to-end from API design and SDK ergonomics to documentation, sample apps, and onboarding flows * Understand our customers deeply by engaging directly with developers and end-users, synthesizing feedback, and using data to drive prioritization * Shape how the world builds AI agents by defining new primitives, patterns, and best practices for agentic applications About the team Our team brings the AGI Lab's agent capabilities to customers. We build accessible, usable products: interfaces, frameworks, and solutions, that turn our platform and model capabilities into AI agents developers can use. We own the Nova Act agent playground, Nova Act IDE extension, Nova Act SDK, Nova Act AWS Console, reference architectures, sample applications, and more.
Applied Scientist (Contract), Artificial General Intelligence
US, WA, Bellevue
This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
Research Scientist, Amazon Music - DISCO
US, WA, Seattle
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Research Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing causal models and randomized controlled trials to assess marketing effectiveness and inform strategic decision-making. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal Models Design, build, and validate causal models to evaluate the impact of marketing campaigns and initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. Conduct Randomized Controlled Trials Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of marketing strategies. Ensure robust experimental design and proper execution to derive credible insights. Statistical Analysis and Inference Perform complex statistical analyses to interpret data from experiments and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. Data-Driven Decision Making Collaborate with marketing teams to provide data-driven recommendations that enhance campaign performance and ROI. Present findings and insights to stakeholders in a clear and actionable manner. Collaborative Problem Solving Work closely with cross-functional teams, including marketing, product, and engineering, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. Stay Current with Industry Trends Keep abreast of the latest developments in data science, causal inference, and marketing analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. Documentation and Reporting Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare reports and presentations that effectively communicate complex analyses to non-technical audiences.
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