“Ambient intelligence" will accelerate advances in general AI

Alexa’s chief scientist on how customer-obsessed science is accelerating general intelligence.

As the world has become more connected, and computing has permeated our surroundings, a new AI paradigm is emerging: ambient intelligence. In this paradigm, our environment responds to our requests and anticipates our needs, provides information or suggests actions, and then recedes into the background.

Rohit Prasad.jpg
Rohit Prasad, Alexa head scientist and senior vice president at Amazon.

This vision of ambient intelligence is not that different from the one on Star Trek. But for most of the last decade, the focus has been reactive assistance — for example, ensuring that customer-initiated requests to Alexa meet customers’ expectations.

In the ambient-intelligence vision, an AI service such as Alexa makes sense of the state of your environment, including devices, sensors, objects, people, and activity around you, to help you in every situation where you need assistance — either reactively (customer initiated) or proactively (AI initiated).

Realizing the ultimate potential of ambient intelligence requires Alexa to bring the best of machine-intelligence capabilities together with the best of human-intelligence capabilities, which is the barometer of general intelligence today.

The most pragmatic definition of general intelligence is the ability to (1) learn multiple tasks jointly, versus modeling each task independently; (2) continually adapt to changes within a set of known tasks, without explicit human supervision; and (3) learn new tasks directly by interacting with end users.

While these general-intelligence characteristics apply to all types of AI systems, for interactive AI services such as Alexa, two more attributes are critical: (1) multisensory and multimodal intelligence — the ability to process data from multiple input sensors (e.g., microphones, cameras, ultrasound), fuse sensor data for improved understanding of customer goals, and generate output in different modalities (e.g., speech, text, image, video); and (2) interaction skills — the ability to converse in a human-like manner, which encompasses not just command of natural language but also the ability to recognize and respond to affect.

What this means for our customers is that Alexa will become

  • More competent: Alexa’s functionalities and skills will expand much faster through multitask intelligence. Additionally, Alexa will improve through self-learning, becoming less reliant on labeled data;
  • More natural and conversational: Alexa interactions will be as free flowing as human interactions through multisensory intelligence, generalizable language models, commonsense reasoning, and affect modeling; 
  • More personalized: Alexa will adapt to each individual using speech and computer vision. Further, customers will be able to directly personalize Alexa explicitly and implicitly;  
  • More insightful and proactive: Alexa will anticipate customer needs through awareness of the shared environment, make suggestions, and even act on customers’ behalf;  
  • More trustworthy:  Alexa will have the same attributes that we cherish in trustworthy people, such as discretion, fairness, and ethical behavior.

In the past year, Alexa has made considerable progress on all these fronts.

More competent

Alexa receives billions of requests per month, and it is critical for it to answer each of these requests to customers’ satisfaction. In 2021, through advances in automatic speech recognition (ASR), natural-language understanding (NLU), and action resolution, Alexa has become 13% more accurate than the previous year — even as the complexity of customer requests has increased.

Alexa has more than 130,000 third-party skills, whose diversity is a testament to their developers’ creativity. Further, it is available in more than 15 language variants across more than 80 countries, most recently Khaleeji Arabic in Saudi Arabia.

Through advances in large pretrained language models, we are making it easier to expand Alexa’s functionality in terms of both skills and languages. Specifically, we have trained an “Alexa Teacher Model,” a large, pretrained, multilingual model with billions of parameters that encodes language as well as salient patterns of interactions with Alexa. Instead of building new task-specific NLU models (e.g., a skill, a feature, or a language) from scratch on task-specific data, we can build them by fine-tuning the Alexa Teacher model, which provides substantial gains in performance from the same amount of task-specific training data.

While today, the Alexa Teacher Model itself is impractical for real-time language understanding, once it is distilled and fine-tuned, it is compact enough to run in real time but remains more accurate than a similar-sized model trained from scratch. The capacity to generalize across tasks, which the language model enables, is one of the hallmarks of general intelligence.

ATM pipeline.png
The Alexa Teacher Model (AlexaTM) pipeline. The Alexa Teacher Model is trained on a large set of GPUs (left), then distilled into smaller variants (center), whose size depends on their uses. The end user adapts a distilled model to its particular use by fine-tuning it on in-domain data (right).

Models derived from the Alexa Teacher Model have helped reduce customer friction in several locales and will help facilitate and scale multilingual and multimodal use cases in coming years.

Still, faster deployment of new functionality is not sufficient. Customer interactions with Alexa are ever evolving, so Alexa needs to improve continuously. To that end, we have expanded Alexa’s self-learning capability — in particular, its ability to automatically learn from implicit feedback, e.g., when a customer cuts Alexa off in order to rephrase a query.

Currently, we have two methods for learning from implicit feedback. One is a mechanism that learns to automatically reformulate the ASR output to ensure a more accurate response, and the other automatically annotates interaction data to enable the retraining of NLU models with minimal human involvement.

At this year’s Conference on Empirical Methods in Natural Language Processing (EMNLP), Alexa AI researchers presented papers reporting our progress on both these fronts.

Learning how to rewrite customer requests requires identifying which successful requests are rephrases of unsuccessful ones. Past work on rephrase detection considered sentences in pairs, determining the likelihood that one is a rephrase of the other. In our EMNLP paper, we explain how to use temporal features of the dialogue history to better identify rephrases, with an accuracy improvement of 28% on one test dataset.

Rephrases.png
Earlier rephrase detection models computed similarity scores between pairs of queries (right), which could lead to inaccuracies. A new model instead uses full dialogue context (left) to more accurately detect rephrases by leveraging session-level semantic information. From “Contextual rephrase detection for reducing friction in dialogue systems”.

In the other paper, we describe a scalable framework for using automatically annotated data to continually update our NLU models. This paper shows how to operationalize our previous work on automatic annotation, to deliver immediate results to our customers.

More natural and conversational

As magical as it is to interact with Alexa by simply saying its name, repeating the name during longer interactions feels unnatural: when we’re talking to other people, we don’t use their names on every turn.

This year, we took a major step toward making interactions with Alexa more natural through Conversation Mode, which leverages Echo Show 10’s camera to enable wake-word-free interactions by improving the detection of device directedness (i.e., the intent of addressing Alexa) — even when there are multiple people in the room, conversing with each other as well as with Alexa.

Conversation Mode uses novel computer vision algorithms to gauge customers’ physical orientations toward the device, which indicate whether they’re addressing Alexa or each other. The combination of visual and audio information dramatically improves device-directed-speech detection relative to either modality used independently. Further, on-device speech recognition using fully neural recurrent-neural-network transducers ensures that Alexa recognizes conversational speech with low latency.

We have also started extending Alexa’s conversational memory, going beyond anaphoric references within an interaction session (e.g., “What is its resolution?” while shopping for TVs) to temporarily maintain memory across sessions in certain situations. For example, for high-consideration purchases such as TVs, Alexa remembers your last interaction and starts off your next interaction where you left off. This capability required us to extend Alexa Conversations, which trains deep-learning-based models on synthetic data automatically generated from a small amount of developer-provided data.

As effective as large neural transformer-based language models are for generating textual responses, they lack the commonsense and knowledge grounding they need to be truly useful in large-scale human-machine interactions. This fall, to help foster the type of invention needed to overcome these challenges, we released the commonsense dialogue dataset, which consists of more than 11,000 newly collected dialogues. In each dialogue, successive turns are related by relationship triples in the public commonsense knowledge graph Conceptnet, such as <doctor, LocateAt, hospital> or <specialist, TypeOf, doctor>.

Commonsense dialogue.png
In each dialogue in the commonsense-dialogue dataset, successive turns are related by relationship triples in the public commonsense knowledge graph Conceptnet, such as <piano, RelatedTo, musical> or <musical, RelatedTo, violin>.

Another way to inject common sense into dialogue models is to enable them to import information from online or other sources as needed, on the fly. At the NeurIPS Workshop on Efficient Natural Language and Speech Processing (ENLSP) earlier this month, Alexa researchers won a best-paper award for doing just that. They propose a few-shot-learning approach to training a knowledge-seeking-turn detector, which can recognize customer questions that can’t be answered through existing API calls.

This year, we also published several papers on affect modeling. At the International Conference on Acoustics, Speech, and Signal Processing, we presented the use of contrastive unsupervised learning to improve emotion recognition when training data is scarce; and at the Spoken Language Technologies conference, we described the adaptation of pretrained language models, which have been so successful at natural-language-processing tasks, to the problem of social and emotional commonsense reasoning.

On the flip side, when human speakers recognize shifts in the emotional states of people they’re talking to, they modify the affect in their responses. At the Speech Synthesis Workshop (SSW11) this summer, we extended our previous work on prosody variation to modify the affective characteristics of synthesized speech.

More personalized

AI’s ability to conform to customers as opposed to the other way around differentiates it from other technological advancements. This fall, we launched multiple new services that allow our customers to personalize AI in a self-serve fashion.

With preference teaching, customers can explicitly teach Alexa which skills should handle weather-related questions, which sports teams they follow, and which cuisines they prefer.

CustomAED_embedding.png
A two-dimensional projection of embeddings produced through Custom Sound Event Detection. New sounds are identified by their location in the embedding space.

With Custom Sound Event Detection, customers can train Alexa to recognize new sounds — such as a doorbell ringing — from just a handful of examples. Custom Sound Event Detection uses proximity in a neural network’s representational space to recognize instances of the same sound.

Custom Event Alerts for Ring Video Doorbell cameras and Spotlight cameras works in a similar way. With just a few examples, customers can train their devices to recognize certain states of affairs in the world — such as a shed door that has been left open.

In August, we introduced adaptive volume for Alexa, which lets Echo devices adjust their volume according to ambient-noise levels, so that the perceived noise level stays consistent for the customer. One of the key elements of the approach is algorithmically separating the speech signal and the noise signal, so that they’re separate inputs to the volume adaptation model.

We also launched adaptive listening for US English, an opt-in feature that gives customers more time to finish speaking before Alexa responds, making Alexa a more accessible, patient listener. For speakers with certain speech impediments, adaptive listening has reduced the friction in their Alexa interactions by more than two-thirds.

Finally, Alexa customers can choose to interact with celebrity personalities such as Amitabh Bachchan, Melissa McCarthy, Samuel L. Jackson, or Shaquille O'Neal. At the end of the year, we even brought holiday cheer to Alexa interactions by launching the festive personality of Santa Claus.

More insightful and proactive

Today, one in four smart-home interactions is initiated by Alexa, due to the expansion of its predictive and proactive features such as hunches and routines.

Since 2018, Alexa hunches have recognized anomalies in customers’ daily routines and suggested corrections — noticing that a light was left on at night and offering to turn it off, for instance. This year, we gave customers the option of making hunches more proactive, so Alexa can act on their behalf. When proactive hunches are enabled, Alexa will turn that light off for you without asking first.

Routines let you initiate a sequence of actions with a single trigger word, rather than issuing the same instructions over and over again. Previously, customers had to specify which actions they wanted to string together. But this year, we began phasing in inferred routines. With inferred routines, Alexa recognizes sequences of actions that customers commonly repeat — such as, say, turning on the kitchen lights, starting the coffee maker, and playing the “Wake Up!” playlist — and suggests combining them into a routine. To save the routine, the customer simply accepts Alexa’s suggestion.

We have also continued to expand latent-goal prediction, where Alexa recognizes the larger customer need implied by an initial request and suggests actions or skills to fulfill that need. For instance, a customer asks, “Who won the Celtics game?”, and after answering, Alexa asks, “Would you like to know when the Celtics are playing next?”

Latent-goal prediction uses pointwise mutual information to measure the likelihood of an interaction pattern in a given context relative to its likelihood across all Alexa traffic, and it uses bandit learning to track whether recommendations are helping or not and suppress underperforming experiences.

We have also introduced visual ID on our latest Echo device, Echo Show 15. With visual ID, Alexa shows notes and other reminders just for you (e.g., “Leave a note for Jack that his new passport has arrived”). Visual ID is also available on Astro, an Alexa-enabled home robot that extends environment and state awareness to your physical space. Astro can follow you playing media or find you to deliver calls, messages, timers, alarms, or reminders. With a Ring Protect prosubscription, Astro can also proactively patrol your home and investigate anomalous activities.

More trustworthy

Preserving customer privacy is an uncompromisable tenet for us and an invention area. Differential privacy in particular is one of our key areas of focus. This year, we won a best-paper award at the annual meeting of the Florida Artificial Intelligence Research Society (FLAIRS) for an approach to improving the performance of machine learning models while still meeting the privacy standards imposed by differential-privacy analysis.

At the Conference of the European Chapter of the Association for Computational Linguistics, we presented a method for protecting privacy by automatically rephrasing training text while preserving their semantic sense, in a way that, again, meets differential-privacy standards.

Biased language models still.jpg
Alexa AI researchers constructed a dataset of more than 23,000 text generation prompts, each consisting of six to nine words of a sentence on Wikipedia. The prompts can be used to test language models for bias.
Credit: Glynis Condon

We want Alexa to work equally well for everyone. To that end, in addition to our partnership with the National Science Foundation in the area of fairness in AI, we are pursuing research into detecting and mitigating inappropriate bias. At the ACM Conference on Fairness, Accountability, and Transparency (FAccT) and the Conference of the European Association for Computational Linguistics, we published a pair of papers on measuring bias in language models and detecting bias in datasets for training models that recognize unreliable news.

The path ahead

I recognize that there are multiple paths to general AI, each with years of fundamental research ahead of it. I believe Alexa and its underlying vision of ambient intelligence offer a pragmatic path to general AI— one where every advancement makes Alexa more useful for our customers in their daily lives.

I am in awe at the rate of invention from the Alexa team in the most difficult circumstances. As we wrap up yet another year of the COVID pandemic, I hope the advances the worldwide community of AI researchers is making in every discipline of AI will help us prevent future pandemics.

Related content

US, WA, Seattle
Job summaryPrime Video is an industry leading, high-growth business and a critical driver of Amazon Prime subscriptions, which contribute to customer loyalty and lifetime value. Prime Video is a digital video streaming and download service that offers Amazon customers the ability to rent, purchase or subscribe to a huge catalog of videos. The Prime Video Economist team works on disruptive ideas in the Prime Video space.We are looking for a truly innovative Data Scientist to work on disruptive ideas within the Prime Video space. Examples of problem spaces you may be working on include video product pricing, ecosystem effects (how streaming affects rentals or purchases), and forecasting demand for new content on the platform.On our team you will work with a diverse scientific team including engineers and economists as well as other data scientist to build statistical models using world-class data systems and partner directly with the business to implement the solutions.Key job responsibilities· Implement code (Python, R, Scala, etc.) for analyzing data and building machine learning/econometric models to solve specific business problems. Work with software engineering teams to productionize algorithms where appropriate.· Lead the development of the scientific roadmap, guide and develop junior engineers in designing and implementing scientific solutions.· Translate analytic insights into concrete, actionable recommendations for business or product improvement. Develop and present these as reports to senior stakeholders with ranging levels of technical knowledge.· Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.· Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building to deliver accurate and effective business insights.· Innovate by researching, learning, and adapting new modeling techniques and procedures to existing business problems.· Manage and execute entire project from start to finish including problem solving, data gathering and manipulation, predictive modeling, and stakeholder engagement.
US, WA, Bellevue
Job summaryDo you enjoy solving challenging problems and driving innovations in research? Are you seeking for an environment with a group of motivated and talented scientists like yourself? Do you want to create scalable optimization models and apply machine learning techniques to guide real-world decisions? Do you want to play a key role in the future of Amazon transportation and operations? Come and join us at Amazon's Modeling and Optimization team (MOP).Key job responsibilitiesAn Applied Scientist in the Modeling and Optimization (MOP) team· provides analytical decision support to Amazon planning teams via applying advanced mathematical and statistical techniques.· collaborates effectively with Amazon internal business customers, and is their trusted partner· is proactive and autonomous in discovering and resolving business pain-points within a given scope· is able to identify a suitable level of sophistication in resolving the different business needs· is confident in leveraging existing solutions to new problems where appropriate and is independent in designing and implementing new solutions where needed· is aware of the limitations of his/her proposed solutions and is proactive in communicating them to the business, and advances the application of sciences towards Amazon business problems by bringing new methods, ideas, and practices to the team and scientific community.A day in the life· Your will be developing model-based optimization, simulation, and/or predictive tools to identify and evaluate opportunities to improve customer experience, network speed, cost, and efficiency of capital investment.· You will quantify the improvements resulting from the application of these tools and you will evaluate the trade-offs between potentially competing objectives.· You will develop good communication skills and ability to speak at a level appropriate for the audience, will collaborate effectively with fellow scientists, software development engineers, and product managers, and will deliver business value in a close partnership with many stakeholders from operations, finance, IT, and business leadership.About the team· At the Modeling and Optimization (MOP) team, we use mathematical optimization, algorithm design, statistics, and machine learning to improve decision-making capabilities across WW Operations and Amazon Logistics.· We focus on transportation topology, labor and resource planning for fulfillment centers (FC), routing science, visualization research, data science and development, and process optimization.· We create models to simulate, optimize, and control the fulfillment network with the objective of reducing cost while improving speed and reliability.· We support multiple business lanes, therefore maintain a comprehensive and objective view, coordinating solutions across organizational lines where possible.
US, WA, Seattle
Job summaryAt Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, result oriented, and driven people. Amazon is seeking a Data Scientist - Simulation to assist in designing and optimizing the fulfillment network concepts and process improvements using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be natural self-starters who have the drive to design, model, and simulate new fulfillment center concepts and processes. The Simulation Data Scientist will be expected to deep dive problems and drive relentlessly towards creative solutions. This individual needs to be comfortable interfacing and driving various functional teams and individuals at all levels of the organization in order to be successful. Perform process modelling and simulation using discrete event simulation software’s, process optimization, statistical data analysis, and Design of Experiments (DOE) etc. to drive decisions on process and designs. Need based remote work option is available.Responsibilities:· Lead system level complex Discrete Event Simulation (DES) projects to build , simulate, and optimize the fulfillment center operational process flow models using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages· Understand process flows , analyze data, perform Design of Experiments and effectively represent in simulation model to achieve better correlation and process improvements· Manage multiple DES simulation projects and tasks simultaneously and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors.· Facilitate process improvement initiatives among site operations, engineering, and corporate systems groups.· Utilize code (python or another object oriented language) for data analysis and modeling algorithms· Analyze historical data to identify trends and support decision making using Statistical Techniques· Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow, process design, and site layout.· Deliver results according to project schedules and quality· Provide written and verbal presentations to share insights and recommendations to audiences of varying levels of technical sophistication.· Make technical trade-offs for long term/short-term needs considering challenges in business area by applying relevant data science disciplines, and interactions among systems.
US, WA, Seattle
Job summaryAmazon is seeking an outstanding Data Scientist to uncover key insights on how customers engage with live sports events on Prime Video globally. With prestigious US sporting matches on Prime Video from NFL’s Thursday Night Football, the WNBA, AVP, the New York Yankees, and the Seattle Sounders, as well as global events like the English Premiere League (UK), UEFA Champions League (Italy, Germany), Ligue 1 (France), US Open Tennis (UK), Roland Garros (France), Autumn Nations Cup Rugby (UK) and more, live sports are an integral and growing component of Prime Video. As our selection of events expands, the Prime Video Content Analytics team is looking to enable agile decision making on live sports by developing key insights into customer engagement with live sport and translating these insights into large scale predictive modeling and analytics solutions.Key job responsibilitiesYou will have the following responsibilities within the scope of our global Prime Video business:· Drive analytics in an uncharted field that is not only developing at a fast pace but also becoming increasingly important to the Prime Video business· Support the analytical needs of stakeholders in the sports, advertising, finance, and live events teams, inclusive of statistical inference, demand modeling, and feature engineering· Build profitability models for new sports rights and partner with finance on business use cases· Think outside the box to use novel data and methodological approaches· Create new metrics that effectively guide the business and deploy dashboards to surface them to senior leadership· Ensure that the quality and timeliness of analytic deliverables meet business expectationsAbout the teamThe Prime Video Content Analytics team uses machine learning, econometrics, and data science to optimize Amazon’s streaming-video catalogue, driving customer engagement and Prime member acquisition. We generate insights to guide Amazon’s digital-video strategy, and we provide direct support to the content-acquisition process. We use detailed customer behavioral data (e.g. streaming history) and detailed information about content (e.g. IMDb-sourced characteristics) to predict and understand what customers like to watch.
ES, M, Madrid
Job summaryAmazon is looking for creative Applied Scientists to tackle some of the most interesting problems on the leading edge of machine learning (ML), search, natural language processing (NLP), and related areas with our Amazon Books team. At Amazon Books we believe that books are not only needed to work, education and entertainment, but are also required for a healthy society. As such, we aim to create an unmatched book discovery experience for our customers worldwide. We enable customers to discover new books, authors and genres through sophisticated recommendation engines, smart search tools and through social interaction, and we need your help to keep innovating in this space.If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you. You will develop and implement novel algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, search, NLP, and deep learning. You will innovate, help move the needle for applied research in these exciting areas and build cutting-edge and scalable technologies that enable delightful experiences for hundreds of millions of people.In this role you will:· Work collaboratively with other scientists and developers to design and implement scalable models for improving our customers' experience discovering and getting the most out of their books;· Have the opportunity to work with a variety of technologies in a variety of use cases;· Drive scalable solutions from the business to prototyping, production testing and through engineering directly to production;· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide other members to achieve their career growth potential.About the teamWe aspire to be experts at the forefront of AI, machine learning and data science and their application to books e-commerce to help engineering teams innovate for readers, authors and publishers.As an Applied Scientist, you'll help us translate customer problems into tractable technical problems, and find ways to solve them by combining your expertise and that of other scientists and team members. You will work with partner engineering and business teams to ensure solutions have a real impact.
US, WA, Seattle
Job summaryAre you inspired by building new technologies to benefit customers? Do you dream of being at the forefront of robotics and autonomous system technology? Would you enjoy working in a fast paced, highly collaborative, start-up like environment? If you answered yes to any of these then you've got to check out the Amazon Scout team.We’ve been hard at work developing a new, fully-electric delivery system – Amazon Scout – designed to get packages to customers using autonomous delivery devices. These devices were created by Amazon, are the size of a small cooler, and roll along sidewalks at a walking pace. We developed Amazon Scout at our research and development lab in Seattle, ensuring the devices can safely and efficiently navigate around pets, pedestrians and anything else in their path.The Amazon Scout team shares a passion for innovation using advanced technologies, a love of solving complex challenges, and a desire to delight customers. We're looking for people who like dealing with ambiguity, solving hard, large scale problems, and working in a startup like environment. To learn more about Amazon Scout, check out our Amazon Day One Blog here: http://amazon.com/scoutAs a part of the localization team you will:· Collaborate closely with engineers, applied researchers and hardware teams to develop computer vision and machine learning algorithms and software for robots.· Take responsibility for technical problem solving, including creatively meeting product objectives and developing best practices.· Interact with teammates in variety of roles to accomplish your goals· Identify and initiate investigations of new technologies, prototype and test solutions for product features, and design and validate designs that deliver an exceptional user experience.· Recruit, hire and develop other applied scientists.
US, WA, Bellevue
Job summaryThe People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists 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. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team.Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions.Key job responsibilitiesUse causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees.A day in the lifeWork with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions.About the teamWe are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, Virtual
Job summaryAmazon’s Global Reliability Team is seeking a Principal Research Scientist to help envision, design and build the next generation of predictive maintenance capabilities and inventory management optimization behind Amazon’s Fulfillment Centers, Transportation Services, and Global Specialty Fulfillment.Key job responsibilitiesThe Principal Research Scientist will partner with senior leadership to develop long term strategic products/solutions and will represent and advocate them to leaders in our organization and other partner organizations such as Amazon Fulfillment Technologies, Workplace Health and Safety, amongst others. They will interact with Amazon scholars and universities among other research institutions to ensure that our team and our senior executives are up to speed on important trends, tools and technologies and how they can be used to impact the business.A day in the lifeIn this role, you will participate and lead the brainstorming sessions and review other scientists’ research. They will actively participate in the science community through presenting their research at the internal and external conference. They will mentor senior scientists for their career development and growth and help the company to identify and acquire scientists with the right skillset.About the teamWe are seeking high-energy individuals that are passionate about working with real-time machine and sensor data to build automated systems aimed to improve equipment availability.This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. Experience in applied analytics is essential, and they should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in reliability engineering, econometrics, statistical inference, and time series modeling.
US, MA, Cambridge
Job summaryAmazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?The Role:We are looking for a high caliber Applied Scientist Lead to join our team. As part of the larger technology team working on new consumer technology, your work will have a large impact to hardware, internal software developers, ecosystem, and ultimately the lives of Amazon customers. In this role, you will:• Lead a team of talented audio scientists and SW developers to bring a new and innovative audio products and services to delight customers• Propose new research projects, get buy-in from stakeholders, plan and budget the project and lead the team for successful execution• Work closely with an inter-disciplinary product development team including outside partners to bring the prototype algorithm into commercialization• Mentor team on music/speech/acoustic processing technology development• Manage small team of world class scientists and SW engineers in audio• Take a big part in the mission to create earth's best employerBe a respectable team leader in an open and collaborative environment
US, MA, Boston
Job summaryAre you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.We seek a talented and motivated engineer to tackle broad challenges in system-level analysis. You will work in a small team to quantify system performance at scale and to expand the breadth and depth of our analysis (e.g. increase the range of software components and warehouse processes covered by our models, develop our library of key performance indicators, construct experiments that efficiently root cause emergent behaviors). You will engage with growing teams of software development and warehouse design engineers to drive evolution of the AR system and of the simulation engine that supports our work.This role is a 6 month co-op to join AR full time (40 hours/week) from July-December 2022. Come join us in North Reading, MA, or in our newly expanded innovation hub in Westborough, MA!Both campuses provide a unique opportunity for co-ops to have direct access to robotics testing labs and manufacturing facilities. Remote and hybrid flexibility is available for this role.