“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.

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.

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.

Research areas

Related content

US, CA, Santa Clara
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solutions About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: San Francisco, CA, USA | Santa Clara, CA, USA
US, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Amazon AGI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. Our goal is to provide conversational, visual and proactive mechanisms to delight customers, increase customer engagement, reduce defects, and enable natural interactions across Amazon touch points (e.g., Alexa, Stores, Mobile). To achieve our mission, we build automated, scalable, self-serve AI systems that use customer, device and ambient signals to offer personalized suggestions, comprehend customer inputs, learn from customer interactions, reduce defects, and propose appropriate actions to serve millions of Amazon customers across the globe. Key job responsibilities As a Principal Applied Scientist, you will be a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You tackle intrinsically hard problems, acquiring expertise as needed and decompose complex problems into straightforward solutions. You align teams toward coherent strategies. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Principal Scientist to help build industry-leading conversational technologies that customers love. Our mission is to push the envelope in Artificial Intelligence (AI), Natural Language Understanding (NLU), Machine Learning (ML), Dialog Management, Automatic Speech Recognition (ASR), and Audio Signal Processing, in order to provide the best-possible experience for our customers We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, NY, New York
We are looking for a motivated and experienced Senior Data Scientist with experience in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture with deep understanding in advertising businesses, to be part of a team of talented scientists and engineers to innovate, iterate, and solve real world problem with cutting-edge AWS technologies. In this role, you will take a leading role in defining the problem, innovating the ML/AI solutions, and information the tech roadmap. You will join a cross-functional, fun-loving team, working closely with scientists and engineers in a daily basis. You will innovate on behalf of our customers by prototyping, delivering functional proofs of concept (POCs), and partnering with our engineers to productize and scale successful POCs. If you are passionate about creating the future, come join us as we have fun, and make history. Key job responsibilities - Define and execute a research & development roadmap that drives data-informed decision making for marketers and advertisers - Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions - Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale - Partner with product managers and stakeholders to define forward-looking product visions and prospective business use cases - Drive and lead of culture of data-driven innovations within and outside across Amazon Ads Marketing orgs About the team Marketing Decision Science provides science products to enable Amazon Ads Marketing to deliver relevant and compelling guidance across marketing channels to prospective and active advertisers for success on Amazon. We own the product, technology and deployment roadmap for AI- and analytics-powered products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right team to help them make the data-informed advertising decisions. Our science-based products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Seattle
Are you excited about developing models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a collaborative Applied Scientist to focus on computer vision machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
Looking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities The Senior Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy. About the team NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Seattle
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS Measurement, targeting, recommendation, forecasting related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with economists, scientists and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing measurement, valuation and machine learning capabilities directly leading to improvements in our key performance metrics. A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning models and causal inference. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers. Key job responsibilities - Lead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization. - Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches. - Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches. - Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines. - Publish and present your work at internal and external scientific venues in the fields of ML and causal inference. - Influence long-term science initiatives and mentor other scientists across AWS. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA
US, WA, Bellevue
Where will Amazon's growth come from in the next year? What about over the next five? Which product lines are poised to quintuple in size? Are we investing enough in our infrastructure, or too much? How do our customers react to changes in prices, product selection, or delivery times? These are among the most important questions at Amazon today. The Topline Forecasting team in the Supply Chain Optimization Technologies (SCOT) group is looking for innovative, passionate and results-oriented Principal Economist to provide thought-leadership to help answer these questions. You will have an opportunity to own the long-run outlook for Amazon’s global consumer business and shape strategic decisions at the highest level. The successful candidate will be able to formalize problem definitions from ambiguous requirements, build econometric models using Amazon’s world-class data systems, and develop cutting-edge solutions for non-standard problems. Key job responsibilities • You understand the state-of-the-art in time series and econometric modeling. • You apply econometric tools and theory to solve business problems in a fast moving environment. • You excel at extracting insights and correct interpretations from data using advanced modeling techniques. • You communicate insights in a digestible manner to senior leaders in Finance and Operations within the company. • You are able to anticipate future business challenges and key questions, and have the ability to design modeling solutions to tackle them. • You have broad influence over the Topline team’s scientific research agenda and deliverables. • You contribute to the broader Econ research community in Amazon. • You advise other economists on scientific best-practices and raise the bar of research. • You will actively mentor other scientists and contribute to their career development. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | New York, NY, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA