Arabic Alexa redone.jpeg
At launch, the Arabic version of Alexa will be available in the Kingdom of Saudi Arabia and the United Arab Emirates.

How Alexa learned Arabic

Arabic posed unique challenges for speech recognition, language understanding, and speech synthesis.

The Arabic version of Alexa launched in December 2021, in the Kingdom of Saudi Arabia and the United Arab Emirates, and like all new Alexa languages, it posed a unique set of challenges.

The first was to decide what forms of Arabic Alexa should speak. While the official written language in KSA and the UAE is Modern Standard Arabic (MSA), in everyday life, Arabic speakers use dialectal forms of Arabic, with many vernacular variations.

For customers, engaging with Alexa in their native dialects would be more natural than speaking MSA. So the Alexa AI team — including computational linguists — determined that Arabic Alexa would be able to understand requests in both MSA and Khaleeji (Gulf) dialects.

Alexa’s speech outputs, too, would be in both MSA and a Khaleeji dialect — MSA for formal speech, such as responses to requests for information, and Khaleeji for less formal speech, such as confirmation of alarm times and music selections. This means that someone issuing Alexa a request in one Arabic dialect might get a response in a different one. But that mirrors the experience that Arabic speakers in the region have with each other.

Al Fatiha.jpg

The core components of a new Alexa model are automatic speech recognition (ASR), which converts speech into text; natural-language understanding (NLU), which interprets the text to initiate actions; and text-to-speech (TTS), which converts NLU outputs into synthesized speech.

A key question for all three components was how to render utterances textually, both as ASR output and TTS input. Written Arabic suppresses short vowel sounds: it would be sort of like spelling the English word “begin” as “bgn”. People are usually able to infer the mssng vwls frm cntxt.

But in formal and educational texts — such as reading primers for children — vowels and some consonantal sounds are indicated by diacritical marks. So the Alexa AI team had to decide whether the ASR output should include diacritics or not.

One of the major differences between dialects is the vowel sounds, so omitting diacritics makes it easier to create a speech representation that’s applicable to all dialects, which is useful for ASR and NLU.

Moreover, there is no published writing in forms of Arabic other than MSA, so there’s no standard orthography for them, either. Asking annotators to add diacritics could introduce more ambiguity than it alleviates. In the end, the Alexa AI team decided that ASR output should use only two diacritics, the shaddah and maddah, because they help with pronunciation accuracy on entity names that pass from ASR through NLU to TTS.

These design decisions had separate implications for the various Alexa AI teams — ASR, NLU, and TTS — and of course, each of the teams faced its own particular challenges as well.

ASR

One of the ASR team’s goals was to provide a consistent output, given the lack of standardized orthography for both dialectal Arabic and foreign loanwords. One of their decisions was to represent loanwords — such as the names of French or American musicians or albums — using Latin script.

ASR researchers.png
L to R: Applied-science manager Volker Leutnant and applied scientists Moe Hethnawi and Bashar Awwad Shiekh Hasan

To that end, they used a so-called catalogue ingestion normalizer, which takes in a catalogue of terms in French and English and converts the corresponding Arabic-script outputs of the ASR model into Latin script.

Applied-science manager Volker Leutnant and his colleagues on the Alexa Speech team — including applied scientists Moe Hethnawi and Bashar Awwad Shiekh Hasan — began with an English acoustic model, which started out better attuned to human speech sounds than a randomly initialized model. They trained it using public datasets of Arabic speech in the target Khaleeji dialects and data from Cleo, an Alexa skill that allows multilingual customers to help train new-language models by responding to voice prompts with open-form utterances. The Cleo data included labeled utterances in additional Arabic dialects, allowing the ASR model to provide more consistent user experience for a wider range of customers.

NLU

An NLU model takes in utterances transcribed by ASR and classifies them according to intent, such as playing music. It also identifies all the slots in the utterance — such as song names or artist names — and their slot values — such as the particular artist name “Ahlam”.

The first thing the NLU model needs to do is to tokenize the input, or split it into semantic units that should be processed separately. In many languages, tokenization happens naturally during ASR. But Arabic uses word affixes — prefixes and suffixes — to convey contextual meanings.

Some of those affixes, such as articles and prepositions — the Arabic equivalents of “the” or “to” — are irrelevant to NLU and can be left attached to their word stems. But some, such as possessives, require independent slot tags. The suffix meaning “my”, for instance, in the Arabic for “my music”, tells the NLU model just which music the customer wants played. Language engineer Yangsook Park and her colleagues designed the tokenizer to split off those important affixes and leave the rest attached to their stems.

Announce breakfast.jpg

The tokenized input passes to the NLU model, which is a trilingual model, able to process inputs in Arabic, French or English. This not only helps the model handle loanwords used in Arabic, but it also enables the transfer of knowledge from French and English, which currently have more abundant training data than Arabic.

Research science manager Karolina Owczarzak and her team at Alexa AI — including research scientists Khadige Abboud, Olga Golovneva, and Christopher DiPersio — resampled the existing Arabic training data to expand the variety of training examples. For instance, their resampling tool replaces the names of artists or songs in existing utterances with other names from the song catalogue.

A crucial consideration was how many resampled utterances with the same basic structure to include in the training data. Using too many examples based on the same template — such as “let me hear <SongName> by <ArtistName>” or “play the <ArtistName> song <SongName>” —could diminish the model’s performance on other classes of utterance.

To compute the optimal number of examples per utterance template, the NLU researchers constructed a measure of utterance complexity, which factored in both the number of slots in the utterance template and the number of possible values per slot. The more complex the utterance template, the more examples it required.

NLU researchers.png
L to R: Language engineer Yangsook Park, research science manager Karolina Owczarzak, and research scientists Khadige Abboud, Olga Golovneva, and Christopher DiPersio

The model-training process began with a BERT-based language model, which was pretrained on all three languages using unlabeled data and the standard language-modeling objective. That is, words of sentences were randomly masked out, and the model learned to predict the missing words from those that remained. In this stage, the NLU team augmented the Arabic dataset with data translated from English by AWS Translate.

Then the researchers trained the model to perform NLU tasks by fine-tuning it on a large corpus of annotated French and English data — that is, utterances whose intents and slots had been labeled. The idea was to use the abundant data in those two languages to teach the model some general principles of NLU processing, which could then be transferred to a model fine-tuned on the sparser labeled Arabic data.

Finally, the model was fine-tuned again on equal amounts of labeled training data in all three languages, to ensure that fine-tuning on Arabic didn’t diminish the model’s performance on the other two languages.

TTS

Whereas diacritics can get in the way of NLU, they’re indispensable to TTS: the Alexa speech synthesizer needs to know precisely which vowel sounds to produce as output. So when the Arabic TTS model gets a text string from one of Alexa’s functions — such as confirmation of a music selection from the music player — it runs it through a diacritizer, which adds the full set of diacritics back in.

TTS team.png
L to R: Software engineer Tarek Badr, applied scientist Fan Yang, and language engineer Merouane Benhassine.

The TTS researchers, led by software engineer Tarek Badr and applied scientist Fan Yang, trained the diacritizer largely on MSA texts, with some supplemental data in Khaleeji dialects, which the Alexa team compiled itself. Inferring the correct diacritics depends on the whole utterance context: as an analogy, whether “crw” represents “craw”, “crew”, or “crow” could usually be determined from context. So the diacritizer model has an attention mechanism that attends over the complete utterance.

Outputs that should be in Khaleeji Arabic then pass through a module that converts the diacritics to representations of the appropriate short-vowels sounds, along with any other necessary transformations. This is a rule-based system that language engineer Merouane Benhassine and his colleagues built to capture the predictable relationships between MSA and Khaleeji Arabic.

The text-to-speech model itself is a neural network, which takes text as input and outputs acoustic waveforms. It takes advantage of the Amazon TTS team’s recent work on expressive speech to endow the Arabic TTS model with a lively, conversational style by default.

A new Alexa language is never simply a new language: it’s a new language targeted to a specific new locale, because customer needs and linguistic practices vary by country. Going forward, the Alexa AI team will continue working to expand Arabic to additional locales — even as it continues to extend Alexa to whole new language families.

Related content

US, CA, Santa Clara
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, NY, New York
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, WA, Seattle
Job summaryHow can we create a rich, data-driven shopping experience on Amazon? How do we build data models that helps us innovate different ways to enhance customer experience? How do we combine the world's greatest online shopping dataset with Amazon's computing power to create models that deeply understand our customers? Recommendations at Amazon is a way to help customers discover products. Our team's stated mission is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". We strive to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.Key job responsibilitiesScaling state of the art techniques to Amazon-scaleWorking independently and collaborating with SDEs to deploy models to productionDeveloping long-term roadmaps for the team's scientific agendaDesigning experiments to measure business impact of the team's effortsMentoring scientists in the departmentContributing back to the machine learning science community
US, NY, New York
Job summaryAmazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop and enable them to take on more complex tasks in the future.A day in the lifeAbout the hiring groupJob responsibilities* Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.* Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.* Report results in a scientifically rigorous way.* Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.
US, NY, New York
Job summary**This job is also open for New York and Palo Alto**This position will be part of the Marketplace Intelligence organization within Sponsored Products. Our team focuses on determining operating points of Sponsored Products to provide efficient and customized shopping experience for shoppers and increased discoverability and business growth for selling partners by developing new measurements, economics methodology, and state-of-the art machine learnt optimization technologies. Our systems, algorithms and strategies operates on one of the most sophisticated advertising marketplaces that evolves from impression to impression and changes from one marketplace to another, across segments of traffic and demand. Key job responsibilitiesAs a seasoned leader, you will build and manage an inter-disciplinary team with scientists, economists, and engineers to develop and manage monetization controls for SP marketplace. The leader will set the vision of pricing strategy, build engineering system and large scale machine learning and optimization models. These models will continuously change operating points based on the feedback of marketplace, shopper and advertisers.This is a rare and exciting opportunity to be a trailblazer at the intersection of cutting edge science, economics, game theory and engineering to impact millions of advertisers. As a hands-on leader of this team, you will be responsible for defining long term business strategies, answer key research questions, discover investment opportunities, develop and deploy innovative machine learning solutions and deliver business results. You will also participate in organizational planning, hiring, mentoring and leadership development. You will be technically fearless and build scalable science and engineering solutions.
US, WA, Seattle
Job summaryThe Amazon Product Classification and Inference Services team is seeking a Sr. Applied Science Manager for leading initiatives for understanding, classifying and inferring product information. Our vision is simple: build AI systems that are capable of a deep product understanding, so we can organize and merchandise products across the Amazon e-commerce catalog worldwide. You will lead a team of experienced Applied Scientists (direct reports) and also a Manager of Applied Science to create models and deliver them into the Amazon production ecosystem. Your efforts will build a robust ensemble of ML techniques that can drive classification of products with a high precision and scale to new countries and languages. The leader will drive investments in cutting edge machine learning: natural language processing, computer vision and artificial intelligence techniques to solve real world problems at scale. We develop Deep Neural Networks as our your daily job and use the team's output to affect the product discovery of the biggest e-tailer in the world. The research findings are directly related to Amazon’s Browse experience and impact million of customers. The team builds solutions ranging from automatic detection of misclassified product information in the ever growing Amazon Catalog, applications for inferring and backfilling product attributes (processing images, text and all the unstructured attributes) in the Amazon catalog to drive true understanding of products at scale. We are looking for an entrepreneurial, experienced Sr. Applied Science Manager who can turn a group of Machine Learning Scientists and Managers (PhD's in NLP, CV) to produce best in class solutions. The ideal candidate has deep expertise in one or several of the following fields: Web search, Applied/Theoretical Machine Learning, Deep Neural Networks, Classification Systems, Clustering, Label Propagation, Natural Language Processing, Computer Vision. S/he has a strong publication record at top relevant academic venues and experience in launching products/features in the industry.Key job responsibilitiesIn this team, you will:Manage business and technical requirements, design, be responsible for the overall coordination, quality, productivity and will be the primary point of contact for world-wide stakeholders of programs and goals that you lead.Partner with scientists, economists, and engineers to help deliver scalable ML scaled models, while building mechanisms to help our customers gain and apply insights, and build road maps for the projects you own.Track service levels and schedule adherence, and ensure the individual stakeholder teams meet and exceed their performance targets.Be expected to discover, define, and apply scientific, engineering, and business best practices.Manage and develop Scientists (direct reports and a Science Manager with a respective team).A day in the lifeYou will lead an Amazon team that builds creative solutions to real world problems. Your team will own devising the strategy and execution plans that power initiatives ranging from: classifying all Amazon products, fact extraction, automatic detection of missing product information, active learning mechanisms for scaling human tasks, building applications for understanding what type of information is critical, building mechanisms to analyze product composition, ingest images, text, and unstructured data to drive deep understanding of products at scale. About the teamThe team's mission is to infer knowledge, understand, classify, derive product facts for all Amazon products entering the Catalog. The work is critical to power the Amazon Taxonomy, Search, Navigation and Detail Page experiences, impacting million of customers. This is an already formed team with experience leading programs spanning services and ML initiatives supporting all countries and languages. The leader collaborates closely with Software Managers, Sr. Leaders, and has exposure to multiple peer teams at Amazon who rely on this team's developments.
US, MA, Westborough
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 who work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences. 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.Amazon.com empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas.This role is a 3-month internship to join AR full-time (40 hours/week) from May 22, 2023 to August 25, 2023. This Amazon Robotics internship opportunities will be Hybrid (2- 3 days onsite) and based out of the Greater Boston Area in Westborough, MA. The campus provides a unique opportunity to have direct access to robotics testing labs and manufacturing facilities.About the teamWe are seeking data scientist interns to help us analyze data, quantify uncertainty, and build machine learning models to make quick prediction.
US, WA, Seattle
Job summaryDo you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.Major responsibilities Use statistical and machine learning techniques to create scalable risk management systemsLearning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trendsDesign, development and evaluation of highly innovative models for risk managementWorking closely with software engineering teams to drive real-time model implementations and new feature creationsWorking closely with operations staff to optimize risk management operations,Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationTracking general business activity and providing clear, compelling management reporting on a regular basisResearch and implement novel machine learning and statistical approaches