Alexa at five: looking back, looking forward

Today is the fifth anniversary of the launch of the Amazon Echo, so in a talk I gave yesterday at the Web Summit in Lisbon, I looked at how far Alexa has come and where we’re heading next.

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This poster of the original Echo device, signed by the scientists and engineers who helped make it possible, hangs in Rohit's office.

Amazon’s mission is to be the earth’s most customer-centric company. With that mission in mind and the Star Trek computer as an inspiration, on November 6, 2014, a small multidisciplinary team launched Amazon Echo, with the aspiration of revolutionizing daily convenience for our customers using artificial intelligence (AI).

Before Echo ushered in the convenience of voice-enabled ambient computing, customers were used to searches on desktops and mobile phones, where the onus was entirely on them to sift through blue links to find answers to their questions or connect to services. While app stores on phones offered “there’s an app for that” convenience, the cognitive load on customers continued to increase.

Alexa-powered Echo broke these human-machine interaction paradigms, shifting the cognitive load from customers to AI and causing a tectonic shift in how customers interact with a myriad of services, find information on the Web, control smart appliances, and connect with other people.

Enhancements in foundational components of Alexa

In order to be magical at the launch of Echo, Alexa needed to be great at four fundamental AI tasks:

  1. Wake word detection: On the device, detect the keyword “Alexa” to get the AI’s attention;
  2. Automatic speech recognition (ASR): Upon detecting the wake word, convert audio streamed to the Amazon Web Services (AWS) cloud into words;
  3. Natural-language understanding (NLU): Extract the meaning of the recognized words so that Alexa can take the appropriate action in response to the customer’s request; and
  4. Text-to-speech synthesis (TTS): Convert Alexa’s textual response to the customer’s request into spoken audio.

Over the past five years, we have continued to advance each of these foundational components. In both wake word and ASR, we’ve seen fourfold reductions in recognition errors. In NLU, the error reduction has been threefold — even though the range of utterances that NLU processes, and the range of actions Alexa can take, have both increased dramatically. And in listener studies that use the MUSHRA audio perception methodology, we’ve seen an 80% reduction in the naturalness gap between Alexa’s speech and human speech.

Our overarching strategy for Alexa’s AI has been to combine machine learning (ML) — in particular, deep learning — with the large-scale data and computational resources available through AWS. But these performance improvements are the result of research on a variety of specific topics that extend deep learning, including

  • semi-supervised learning, or using a combination of unlabeled and labeled data to improve the ML system;
  • active learning, or the learning strategy where the ML system selects more-informative samples to receive manual labels;
  • large-scale distributed training, or parallelizing ML-based model training for efficient learning on a large corpus; and
  • context-aware modeling, or using a wide variety of information — including the type of device where a request originates, skills the customer uses or has enabled, and past requests — to improve accuracy.

For more coverage of the anniversary of the Echo's launch, see "Alexa, happy birthday" on Amazon's Day One blog.

Customer impact

From Echo’s launch in November 2014 to now, we have gone from zero customer interactions with Alexa to billions per week. Customers now interact with Alexa in 15 language variants and more than 80 countries.

Through the Alexa Voice Service and the Alexa Skills Kit, we have democratized conversational AI. These self-serve APIs and toolkits let developers integrate Alexa into their devices and create custom skills. Alexa is now available on hundreds of different device types. There are more than 85,000 smart-home products that can be controlled with Alexa, from more than 9,500 unique brands, and third-party developers have built more than 100,000 custom skills.

Ongoing research in conversational AI

Alexa’s success doesn’t mean that conversational AI is a solved problem. On the contrary, we’ve just scratched the surface of what’s possible. We’re working hard to make Alexa …

1. More self-learning

Our scientists and engineers are making Alexa smarter faster by reducing reliance on supervised learning (i.e., building ML models on manually labeled data). A few months back, we announced that we’d trained a speech recognition system on a million hours of unlabeled speech using the teacher-student paradigm of deep learning. This technology is now in production for UK English, where it has improved the accuracy of Alexa’s speech recognizers, and we’re working to apply it to all language variants.

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In the teacher-student paradigm of deep learning, a powerful but impractically slow teacher model is trained on a small amount of hand-labeled data, and it in turn annotates a much larger body of unlabeled data to train a leaner, more efficient student model.

This year, we introduced a new self-learning paradigm that enables Alexa to automatically correct ASR and NLU errors without any human annotator in the loop. In this novel approach, we use ML to detect potentially unsatisfactory interactions with Alexa through signals such as the customer’s barging in on (i.e., interrupting) Alexa. Then, a graphical model trained on customers’ paraphrases of their requests automatically revises failing requests into semantically equivalent forms that work.

For example, “play Sirius XM Chill” used to fail, but from customer rephrasing, Alexa has learned that “play Sirius XM Chill” is equivalent to “play Sirius Channel 53” and automatically corrects the failing variant.

Using this implicit learning technique and occasional explicit feedback from customers — e.g., “did you want/mean … ?” — Alexa is now self-correcting millions of defects per week.

2. More natural

In 2015, when the first third-party skills began to appear, customers had to invoke them by name — e.g., “Alexa, ask Lyft to get me a ride to the airport.” However, with tens of thousands of custom skills, it can be difficult to discover skills by voice and remember their names. This is a unique challenge that Alexa faces.

To address this challenge, we have been exploring deep-learning-based name-free skill interaction to make skill discovery and invocation seamless. For several thousands of skills, customers can simply issue a request — “Alexa, get me a ride to the airport” — and Alexa uses information about the customer’s context and interaction history to decide which skill to invoke.

Another way we’ve made interacting with Alexa more natural is by enabling her to handle compound requests, such as “Alexa, turn down the lights and play music”. Among other innovations, this required more efficient techniques for training semantic parsers, which analyze both the structure of a sentence and the meanings of its parts.

Alexa’s responses are also becoming more natural. This year, we began using neural networks for text-to-speech synthesis. This not only results in more-natural-sounding speech but makes it much easier to adapt Alexa’s TTS system to different speaking styles — a newscaster style for reading the news, a DJ style for announcing songs, or even celebrity voices, like Samuel L. Jackson’s.

3. More knowledgeable

Every day, Alexa answers millions of questions that she’s never been asked before, an indication of customers’ growing confidence in Alexa’s question-answering ability.

The core of Alexa’s knowledge base is a knowledge graph, which encodes billions of facts and has grown 20-fold over the past five years. But Alexa also draws information from hundreds of other sources.

And now, customers are helping Alexa learn through Alexa Answers, an online interface that lets people add to Alexa’s knowledge. In a private beta test and the first month of public release, Alexa customers have furnished Alexa Answers with hundreds of thousands of new answers, which have been shared with customers millions of times.

4. More context-aware and proactive

Today, through an optional feature called Hunches, Alexa can learn how you interact with your smart home and suggest actions when she senses that devices such as lights, locks, switches, and plugs are not in the states that you prefer. We are currently expanding the notion of Hunches to include another Alexa feature called Routines. If you set your alarm for 6:00 a.m. every day, for example, and on waking, you immediately ask for the weather, Alexa will suggest creating a Routine that sets the weekday alarm to 6:00 and plays the weather report as soon as the alarm goes off.

Earlier this year, we launched Alexa Guard, a feature that you can activate when you leave the house. If your Echo device detects the sound of a smoke alarm, a carbon monoxide alarm, or glass breaking, Alexa Guard sends you an alert. Guard’s acoustic-event-detection model uses multitask learning, which reduces the amount of labeled data needed for training and makes the model more compact.

This fall, we will begin previewing an extended version of Alexa Guard that recognizes additional sounds associated with activity, such as footsteps, talking, coughing, or doors closing. Customers can also create Routines that include Guard — activating Guard automatically during work hours, for instance.

5. More conversational

Customers want Alexa to do more for them than complete one-shot requests like “Alexa, play Duke Ellington” or “Alexa, what’s the weather?” This year, we have improved Alexa’s ability to carry context from one request to another, the way humans do in conversation.

For instance, if an Alexa customer asks, “When is The Addams Family playing at the Bijou?” and then follows up with the question “Is there a good Mexican restaurant near there?”, Alexa needs to know that “there” refers to the Bijou. Some of our recent work in this area won one of the two best-paper awards at the Association for Computational Linguistics’ Workshop on Natural-Language Processing for Conversational AI. The key idea is to jointly model the salient entities with transformer networks that use a self-attention mechanism.

However, completing complex tasks that require back-and-forth interaction and anticipation of the customer’s latent goals is still a challenging problem. For example, a customer using Alexa to plan a night out would have to use different skills to find a movie, a restaurant near the theater, and a ride-sharing service, coordinating times and locations.

We are currently testing a new deep-learning-based technology, called Alexa Conversations, with a small group of skill developers who are using it to build high-quality multiturn experiences with minimal effort. The developer supplies Alexa Conversations with a set of sample dialogues, and a simulator expands it into 100 times as much data. Alexa Conversations then uses that data to train a bleeding-edge deep-learning model to predict dialogue actions, without the need for a priori hand-authored rules.

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Dialogue management involves tracking the values of "slots", such as time and location, throughout a conversation. Here, blue arrows indicate slots whose values must be updated across conversational turns.

At re:MARS, we demonstrated a new Night Out planning experience that uses Alexa Conversations technology and novel skill-transitioning algorithms to automatically coordinate conversational planning tasks across multiple skills.

We’re also adapting Alexa Conversations technology to the new concierge feature for Ring video doorbells. With this technology, the doorbell can engage in short conversations on your behalf, taking messages or telling a delivery person where to leave a package. We’re working hard to bring both of these experiences to customers.

What will the next five years look like?

Five years ago, it was inconceivable to us that customers would be interacting with Alexa billions of times per week and that developers would, on their own, build 100,000-plus skills. Such adoption is inspiring our teams to invent at an even faster pace, creating novel experiences that will increase utility and further delight our customers.

1. Alexa everywhere

The Echo family of devices and Alexa’s integration into third-party products has made Alexa a part of millions of homes worldwide. We have been working arduously on bringing the convenience of Alexa, which revolutionized daily convenience in homes, to our customers on the go. Echo Buds, Echo Auto, and the Day 1 Editions of Echo Loop and Echo Frames are already demonstrating that Alexa-on-the-go can simplify our lives even further.

With greater portability comes greater risk of slow or lost Internet connections. Echo devices with built-in smart-home hubs already have a hybrid mode, which allows them to do some spoken-language processing when they can’t rely on Alexa’s cloud-based models. This is an important area of ongoing research for us. For instance, we are investigating new techniques for compressing Alexa’s machine learning models so that they can run on-device.

The new on-the-go hardware isn’t the only way that Alexa is becoming more portable. The new Guest Connect experience allows you to log into your Alexa account from any Echo device — even ones you don’t own — and play your music or preferred news.

2. Moving up the AI stack

Alexa’s unparalleled customer and developer adoption provides new challenges for AI research. In particular, to further shift the cognitive load from customers to AI, we must move up the AI stack, from predictions (e.g., extracting customers’ intents) to more contextual reasoning.

One of our goals is to seamlessly connect disparate skills to increase convenience for our customers. Alexa Conversations and the Night Out experience are the first steps in that direction, completing complex tasks across multiple services and skills.

To enable the same kind of interoperability across different AIs, we helped found the Voice Interoperability Initiative, a consortium of dozens of tech companies uniting to promote customer choice by supporting multiple, interoperable voice services on a single device.

Alexa will also make better decisions by factoring in more information about the customer’s context and history. For instance, when a customer asks an Alexa-enabled device in a hotel room “Alexa, what are the pool hours?”, Alexa needs to respond with the hours for the hotel pool and not the community pool.

We are inspired by the success of learning directly from customers through the self-learning techniques I described earlier. This is an important area where we will continue to incorporate new signals, such as vocal frustration with Alexa, and learn from direct and indirect feedback to make Alexa more accurate.

3. Alexa for everyone

As AI systems like Alexa become an indispensable part of our social fabric, bias mitigation and fairness in AI will require even deeper attention. Our goal is for Alexa to work equally well for all our customers. In addition to our own research, we’ve entered into a three-year collaboration with the National Science Foundation to fund research on fairness in AI.

We envision a future where anyone can create conversational-AI systems. With the Alexa Skills Kit and Alexa Voice Service, we made it easy for developers to innovate using Alexa’s AI. Even end users can build personal skills within minutes using Alexa Skill Blueprints.

We are also thrilled with the Alexa Prize competition, which is democratizing conversational AI by letting university students perform state-of-the-art research at scale. University teams are working on the ultimate conversational-AI challenge of creating socialbots that can converse coherently and engagingly for 20 minutes with humans on a range of current events and popular topics”.

The third instance of the challenge is under way, and we are confident that the university teams will continue to push boundaries — perhaps even give their socialbots an original sense of humor, by far one of the hardest AI challenges.

Together with developers and academic researchers, we’ve made great strides in conversational AI. But there’s so much more to be accomplished. While the future is difficult to predict, one thing I am sure of is that the Alexa team will continue to invent on behalf of our customers.

About the Author
Rohit Prasad is VP and head scientist for Alexa AI.

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Are you interested in delighting customers and are passionate about promoting sustainability? Then Amazon’s Packaging Team is the place for you. We are the Customer Packaging EXperience (CPEX) team and we optimize Amazon’s packaging solutions. To do this across billions of shipments, we are looking for someone with statistics and machine learning skills to design, test, implement and maintain packaging decision mechanisms across Amazon.You'll be responsible for the design, implementation, operation, and support of large-scale, performance-critical data science and machine learning systems which help to choose the optimal packaging types for our products. These technologies utilize every quadrant of data science including statistics, NLP, and CV to make the right decision on millions of products. You will work with scientists, software developers, business intelligence engineers, and product managers on our team as well as partner with packaging automation, concessions, and sustainability teams.
US, WA, Seattle
Have you ever ordered a product on Amazon and wondered how that box with a smile arrived at your doorstep so fast? Wondered where it came from and how much it cost Amazon? If so, the Amazon Global Supply Chain Optimization organization is for you.Watch this video to learn more about our organization, SCOT: http://bit.ly/amazon-scotWe are the most customer-centric company on Earth. We need exceptionally talented, bright, and driven people to continue to raise the bar on customer experience.Our objective is to build an experience where you can find and buy anything online and have it delivered fast! We continue to innovate with delivery speed initiatives so that Amazon will continue to own ‘fast’ in the minds of our customers. We are looking for a dynamic, organized self-starter to join as a Research Scientist, who will create state of the art models on Speed initiatives and programs and develop the measurement criteria for success.The AIM (Automated Inventory Management) team in the Supply Chain Optimization Technologies (SCOT) organization is dedicated to answering key strategic questions using quantitative and statistical methods. We develop cutting edge data pipelines, build accurate predictive models, and deploy automated software solutions to provide insights to business leaders at the most senior levels throughout the company. We are looking for a talented, driven, and analytical researcher to help us answer these strategic questions.The AIM Science team leads the way in developing innovative models, algorithms and strategies that will help us gain insights into how our business will grow and what will the drivers of such growth. These predictive models and insights will be based along products and product categories, customer segments, regions and locations, etc.This Research Scientist role will explore and develop innovative quantitative approaches and models, generate features, test hypotheses, design experiments, build predictive models, and work with very large complex data sets in order to explore relationship between business outcomes and key drivers, and then predict the trend on those business outcomes. These predictions and insights will provide a foundation of the highest level of visibility and importance for Amazon's financial and operational planning. The successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges, and possesses strong communication skills to effectively interface between technical and business teams, working together with Software Engineers, Product Managers, Business Analysts and other Scientists.Key Responsibilities:· Research, develop and build predictive models for Amazon business metrics with the goal of higher customer satisfactions. Analyze and research features and engineer features that help support predictive models to connect the dots among different functions such as inventory, speed of delivery, and best selections.. Provide insights by analyzing historical data· Constructively critique peer research and mentor junior scientists and engineers.· Create experiments and prototype implementations of new learning algorithms and prediction techniques.· Collaborate with engineering teams to design and implement software solutions for science problems.· Contribute to progress of the Amazon and broader research communities by producing publications.Amazon is an Equal Opportunity Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
US, WA, Virtual Location - Washington
Amazon has built a reputation for excellence with recent examples of being named the #1 most trusted company for customers. The Selling Partner Abuse team's mission is to protect the trust of our stores for customers and selling partners. We see ourselves as the steward of customer trust and Amazon brand.We are seeking a Machine Learning Science Manager who will lead a team of Applied Scientists, Research Scientists and Data scientists to research and develop innovative machine learning and science solutions to prevent the bad activities in the Amazon stores.Your responsibilities will include:· Build and develop the core science team for Selling Partner Abuse· Create new projects to drive significant business impact and research advancement· Create project milestone and manage multiple projects end-to-end while quickly adapt to changing priorities and generate innovative solutions in an extremely fast-paced environment· Coach the team and continuously raise the bar on highest standard· Build a strong partnership across different business, engineering and science stakeholders· Manage different Machine Learning projects that cover different science areas such as Ensemble Tree learning models, Clustering, Anomaly Detection, Graph models, NLP models, Semi-supervised Learning models and Reinforcement Learning
AU, VIC, Melbourne
Amazon delights millions of customers around the world. Meet the behind the scenes team that enables our Human Resource and Operations Leaders to make informed decisions. The Amazon PeopleInsight team builds reporting and analytics tools for our teams that fulfill customer promise every day. Whether it is Fulfillment Center team that delivers your Prime order in two days, our Amazon Locker team that lets you pick up your package anytime that is convenient for you, our Prime Now team getting you lunch in under an hour, or one of many more, the PeopleInsight group is there providing people metrics along the employee lifecycle for our global operations businesses. The PeopleInsight team is a collaborative group of Business Analysts, Business Intelligence Engineers, Data Engineers, Data Scientists, Product Managers, and Program Managers dedicated to empowering leaders and enabling action through data and science. We deliver workforce, associate experience, and leadership insights so Amazon leaders can focus their efforts in ways that will engage, retain and grow their associates.We are now recruiting for an exceptional Data Scientist, Worldwide OperationsThe ideal candidate will be:· A Well-Rounded Athlete –Like a true athlete, you understand that we succeed or fail as a team. You are always ready to step up beyond your core responsibilities and go the extra mile for the project and your team. You nimbly overcome barriers to deliver the best products more quickly than expected.· A Perpetual Student – You seek knowledge and insight. You challenge yourself to turn moments into master’s classes. Whether closing a gap, developing a new skill, or staying ahead of your industry, you revel in the joy of learning and growing.· A Skilled Communicator – You excel when interacting with business and technical partners whether you are chatting, sending a written message, or conducting a presentation.· A Trusted Advisor – You work closely with stakeholders to define key business needs and deliver on commitments. You enable effective decision making by retrieving and aggregating data from multiple sources and compiling it into a digestible and actionable format.· An Inventor at Heart – You innovate on behalf of your customer by proactively implementing improvements, enhancements, and customizations. Your customers marvel at your creative solutions to challenges they had not yet identified.· A Fearless Explorer – You are drawn to take on the hardest problems, navigate ambiguity, and battle skepticism. You never settle, even in the face of overwhelming obstacles.Roles and ResponsibilitiesSuccess in this role will include influencing within your team and mentoring peers. The problems you will consider will be difficult to solve and often require a range of data science methodologies combined with subject matter expertise. You will need to be capable of gathering and using complex data set across domains. You will deliver artifacts on medium size projects, define the methodology, and own the analysis. Your findings will affect important business decisions. Solutions are testable and reproducible. You will create documents and share findings in line with scientific best practices for both technical and nontechnical audiences.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
US, CA, Virtual Location - California
The Amazon Economics Team is hiring Interns in Economics. 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 Stata or R is necessary, and experience with SQL, UNIX, and Sawtooth 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, data scientists and MBAʼs. 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 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, A9 Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search team works to maximize the quality and trustworthiness of the search experience for visitors to Amazon websites worldwide.Our mission is to provide customers' trust and confidence in Amazon Search shopping experience. We identify problems that are customer trust busters at Amazon, deliver scalable and responsive solutions to these issues, and build experiences that gain customer trust using advanced machine learning methods. We carefully monitor the trustworthiness of the search results and dive deep when we see an unusual pattern. Most of the models used by our team is semi-supervised or unsupervised using small amount of labeled data.In this role you will leverage your strong statistical background to help build the next generation of our machine learning methods to discover untrustworthy search engagements, unsual patterns, and estimate a probability of risk for each item. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models, graph algorithms, and information retrieval algorithms at scale. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data 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 like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses.If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.Major responsibilities· · Use machine learning and analytical techniques to create scalable solutions for business problems· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· · Design, development, evaluate and deploy innovative and highly scalable models for predictive learning· · Research and implement novel machine learning and statistical approaches· · Work closely with software engineering teams to drive real-time model implementations and new feature creations· · Work closely with business owners and operations staff to optimize various business operations· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· · Mentor other scientists and engineers in the use of ML techniques
US, MA, Cambridge
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLCJob Title: Applied Scientist IILocation: Cambridge, MAPosition Responsibilities:Designing, developing, evaluating, deploying, and updating data-driven models and analytical solutions for speech and language understanding applications. Developing practical solutions to complex problems. Translating business and functional requirements into concrete deliverables and building quick prototypes or proofs of concept with team members. Solving technical challenges related to speech and language understanding technologies. Mentoring junior engineers and scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
IN, KA, Bangalore
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.The Moderation and Relevance System (MARS) team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages.In this role, you will build and develop ML models to address content intelligence problems, build advanced algorithms in detecting and generating content. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. You will propose hypotheses, validate these offline and run A/B tests to validate them online. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.