Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Credit: Flavia Loreto

Artificial Intelligence—The revolution hasn’t happened yet

Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley, writes about the classical goals in human-imitative AI, and reflects on how in the current hubbub over the AI revolution it is easy to forget that these goals haven’t yet been achieved. This article is reprinted with permission from the Harvard Data Science Review, where it first appeared.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists—here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

There is a different narrative that one can tell about the current era. Consider the following story, which involves humans, computers, data, and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to one in 20.” She let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis, but amniocentesis was risky—the chance of killing the fetus during the procedure was roughly one in 300. Being a statistician, I was determined to find out where these numbers were coming from. In my research, I discovered that a statistical analysis had been done a decade previously in the UK in which these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. I returned to tell the geneticist that I believed that the white spots were likely false positives, literal white noise.

She said, “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago. That’s when the new machine arrived.”

We didn’t do the amniocentesis, and my wife delivered a healthy girl a few months later, but the episode troubled me, particularly after a back-of-the-envelope calculation convinced me that many thousands of people had gotten that diagnosis that same day worldwide, that many of them had opted for amniocentesis, and that a number of babies had died needlessly. The problem that this episode revealed wasn’t about my individual medical care; it was about a medical system that measured variables and outcomes in various places and times, conducted statistical analyses, and made use of the results in other situations. The problem had to do not just with data analysis per se, but with what database researchers call provenance—broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight.

I’m also a computer scientist, and it occurred to me that the principles needed to build planetary-scale inference-and-decision-making systems of this kind, blending computer science with statistics, and considering human utilities, were nowhere to be found in my education. It occurred to me that the development of such principles—which will be needed not only in the medical domain but also in domains such as commerce, transportation, and education—were at least as important as those of building AI systems that can dazzle us with their game-playing or sensorimotor skills.

Whether or not we come to understand ‘intelligence’ any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While some view this challenge as subservient to the creation of artificial intelligence, another more prosaic, but no less reverent, viewpoint is that it is the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and to do so safely. Whereas civil engineering and chemical engineering built upon physics and chemistry, this new engineering discipline will build on ideas that the preceding century gave substance to, such as information, algorithm, data, uncertainty, computing, inference, and optimization. Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.

While the building blocks are in place, the principles for putting these blocks together are not, and so the blocks are currently being put together in ad-hoc ways. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans, and the environment. Just as early buildings and bridges sometimes fell to the ground—in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws.

Unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. What we’re missing is an engineering discipline with principles of analysis and design.

The current public dialog about these issues too often uses the term AI as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. Let us consider more carefully what AI has been used to refer to, both recently and historically.

Most of what is labeled AI today, particularly in the public sphere, is actually machine learning (ML), a term in use for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions, and help make decisions. In terms of impact on the real world, ML is the real thing, and not just recently. Indeed, that ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical, back-end problems in fraud detection and supply-chain prediction, and building innovative consumer-facing services such as recommendation systems. As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase ‘data science’ emerged to refer to this phenomenon, reflecting both the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, as well as reflecting the larger social and environmental scope of the resulting systems.This confluence of ideas and technology trends has been rebranded as ‘AI’ over the past few years. This rebranding deserves some scrutiny.

Historically, the phrase “artificial intelligence” was coined in the late 1950s to refer to the heady aspiration of realizing in software and hardware an entity possessing human-level intelligence. I will use the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificially intelligent entity should seem to be one of us, if not physically then at least mentally (whatever that might mean). This was largely an academic enterprise. While related academic fields such as operations research, statistics, pattern recognition, information theory, and control theory already existed, and often took inspiration from human or animal behavior, these fields were arguably focused on low-level signals and decisions. The ability of, say, a squirrel to perceive the three-dimensional structure of the forest it lives in, and to leap among its branches, was inspirational to these fields. AI was meant to focus on something different: the high-level or cognitive capability of humans to reason and to think. Sixty years later, however, high-level reasoning and thought remain elusive. The developments now being called AI arose mostly in the engineering fields associated with low-level pattern recognition and movement control, as well as in the field of statistics, the discipline focused on finding patterns in data and on making well-founded predictions, tests of hypotheses, and decisions.

Indeed, the famous backpropagation algorithm that David Rumelhart rediscovered in the early 1980s, and which is now considered at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon.

Since the 1960s, much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. Rather, as in the case of the Apollo spaceships, these ideas have often hidden behind the scenes, the handiwork of researchers focused on specific engineering challenges. Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success—these advances have powered companies such as Google, Netflix, Facebook, and Amazon.

One could simply refer to all of this as AI, and indeed that is what appears to have happened. Such labeling may come as a surprise to optimization or statistics researchers, who find themselves suddenly called AI researchers, but labels aside, the bigger problem is that the use of this single, ill-defined acronym prevents a clear understanding of the range of intellectual and commercial issues at play.

The past two decades have seen major progress—in industry and academia—in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). Here computation and data are used to create services that augment human intelligence and creativity. A search engine can be viewed as an example of IA, as it augments human memory and factual knowledge, as can natural language translation, which augments the ability of a human to communicate. Computer-based generation of sounds and images serves as a palette and creativity enhancer for artists. While services of this kind could conceivably involve high-level reasoning and thought, currently they don’t; they mostly perform various kinds of string-matching and numerical operations that capture patterns that humans can make use of.

Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data, and physical entities exists that makes human environments more supportive, interesting, and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce, and finance, with implications for individual humans and societies. This emergence sometimes arises in conversations about an Internet of Things, but that effort generally refers to the mere problem of getting ‘things’ onto the Internet, not to the far grander set of challenges associated with building systems that analyze those data streams to discover facts about the world and permit ‘things’ to interact with humans at a far higher level of abstraction than mere bits.

For example, returning to my personal anecdote, we might imagine living our lives in a societal-scale medical system that sets up data flows and data-analysis flows between doctors and devices positioned in and around human bodies, thereby able to aid human intelligence in making diagnoses and providing care. The system would incorporate information from cells in the body, DNA, blood tests, environment, population genetics, and the vast scientific literature on drugs and treatments. It would not just focus on a single patient and a doctor, but on relationships among all humans, just as current medical testing allows experiments done on one set of humans (or animals) to be brought to bear in the care of other humans. It would help maintain notions of relevance, provenance, and reliability, in the way that the current banking system focuses on such challenges in the domain of finance and payment. While one can foresee many problems arising in such a system—privacy issues, liability issues, security issues, etc.—these concerns should be viewed as challenges, not show-stoppers.

We now come to a critical issue: is working on classical human-imitative AI the best or only way to focus on these larger challenges? Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI—areas such as computer vision, speech recognition, game-playing, and robotics. Perhaps we should simply await further progress in domains such as these. There are two points to make here. First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited; we are very far from realizing human-imitative AI aspirations. The thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering.

Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. On the sufficiency side, consider self-driving cars. For such technology to be realized, a range of engineering problems will need to be solved that may have little relationship to human competencies (or human lack-of-competencies). The overall transportation system (an II system) will likely more closely resemble the current air-traffic control system than the current collection of loosely coupled, forward-facing, inattentive human drivers. It will be vastly more complex than the current air-traffic control system, specifically in its use of massive amounts of data and adaptive statistical modeling to inform fine-grained decisions. Those challenges need to be in the forefront versus a potentially distracting focus on human-imitative AI.

As for the necessity argument, some say that the human-imitative AI aspiration subsumes IA and II aspirations, because a human-imitative AI system would not only be able to solve the classical problems of AI (e.g., as embodied in the Turing test), but it would also be our best bet for solving IA and II problems. Such an argument has little historical precedent. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? Should chemical engineering have been framed in terms of creating an artificial chemist? Even more polemically: if our goal was to build chemical factories, should we have first created an artificial chemist who would have then worked out how to build a chemical factory?

A related argument is that human intelligence is the only kind of intelligence we know, thus we should aim to mimic it as a first step. However, humans are in fact not very good at some kinds of reasoning—we have our lapses, biases, and limitations. Moreover, critically, we did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts. One could argue that an AI system would not only imitate human intelligence, but also correct it, and would also scale to arbitrarily large problems. Of course, we are now in the realm of science fiction—such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.

It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. Such systems must cope with cloud-edge interactions in making timely, distributed decisions, and they must deal with long-tail phenomena where there is lots of data on some individuals and little data on most individuals. They must address the difficulties of sharing data across administrative and competitive boundaries. Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods. Such II systems can be viewed as not merely providing a service, but as creating markets. There are domains such as music, literature, and journalism that are crying out for the emergence of such markets, where data analysis links producers and consumers. And this must all be done within the context of evolving societal, ethical, and legal norms.

Of course, classical human-imitative AI problems remain of great interest as well. However, the current focus on doing AI research via the gathering of data, the deployment of deep learning infrastructure, and the demonstration of systems that mimic certain narrowly defined human skills—with little in the way of emerging explanatory principles—tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the AI revolution it is easy to forget that they are not yet solved.

IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean).

It was John McCarthy (while a professor at Dartmouth, and soon to take a position at MIT) who coined the term AI, apparently to distinguish his budding research agenda from that of Norbert Wiener (then an older professor at MIT). Wiener had coined “cybernetics” to refer to his own vision of intelligent systems—a vision that was closely tied to operations research, statistics, pattern recognition, information theory, and control theory. McCarthy, on the other hand, emphasized the ties to logic. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.)

Beyond the historical perspectives of McCarthy and Wiener, we need to realize that the current public dialog on AI—which focuses on narrow subsets of both industry and of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA, and II.

This scope is less about the realization of science-fiction dreams or superhuman nightmares, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives. Moreover, in this understanding and shaping, there is a need for a diverse set of voices from all walks of life, not merely a dialog among the technologically attuned. Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard.

While industry will drive many developments, academia will also play an essential role, not only in providing some of the most innovative technical ideas, but also in bringing researchers from the computational and statistical disciplines together with researchers from other disciplines whose contributions and perspectives are sorely needed—notably the social sciences, the cognitive sciences, and the humanities.

On the other hand, while the humanities and the sciences are essential as we go forward, we should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope; society is aiming to build new kinds of artifacts. These artifacts should be built to work as claimed. We do not want to build systems that help us with medical treatments, transportation options, and commercial opportunities only to find out after the fact that these systems don’t really work, that they make errors that take their toll in terms of human lives and happiness. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data- and learning-focused fields. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline.

We should embrace the fact that we are witnessing the creation of a new branch of engineering. The term engineering has connotations—in academia and beyond—of cold, affectless machinery, and of loss of control for humans, but an engineering discipline can be what we want it to be. In the current era, we have a real opportunity to conceive of something historically new: a human-centric engineering discipline. I will resist giving this emerging discipline a name, but if the acronym AI continues to serve as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype, and recognize the serious challenges ahead.

Research areas

Amazon Science Newsletter Project Kuiper.jpg
Get more from Amazon Science
Sign up for our monthly newsletter

Work with us

See More Jobs
US, WA, Seattle
Business/Team IntroductionThe Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a Data Scientist with strong analytical and communication skills to join our team. SCOT manages Amazon's inventory under uncertainty of demand, pricing, promotions, supply, vendor lead times, and product life cycle. We optimize complex trade-offs between customer experience, inventory costs, fulfillment costs, fulfillment center capacity, etc. We develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and other data from our product catalog in order to automatically act on millions of dollars’ worth of inventory weekly and establish plans for tens of thousands of employees. As a Data Scientist, you will contribute to the research community, by working with other scientists across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers. SCOT also engages in cutting edge research that we try to share with the community. Recent work from SCOT includes papers presented at the NIPS 2017 Time Series Workshop, SSRN, KDD 2018 Time Series Workshop, and ICML 2018 Deep Generative Models Workshop.Data Scientist ResponsibilitiesAs a Data Scientist in SCOT, will be tasked to understand and work with bleeding edge research to enable the implementation of sophisticated models on big data. As a successful data scientist in the SCOT team, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.Major responsibilities include:· Analysis of large amounts of data from different parts of the supply chain and their associated business functions· Improving upon existing machine learning methodologies by developing new data sources, developing and testing model enhancements, running computational experiments, and fine-tuning model parameters for new models· Formalizing assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them· Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our research, as well as sharing insights and recommendations· Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms
US, WA, Seattle
Global Talent Management (GTM) is centrally responsible for creating and evolving Amazon’s human capital and talent programs and processes.People Science Team within GTM is a growing start-up team with direct impact on Amazonians across all of our businesses and locations around the world. We play a crucial role in ensuring top notch data products and insights facilitate our growth and development of talent in intelligent and curious ways. We regularly use data to pitch ideas and drive conversations with Amazon’s Senior Vice President of HR and other executives about how to improve existing talent programs to solve organizational problems focused on (but not limited to) talent differentiation, talent movement, employee-role matching, product integration, promotion practices, organization design and succession planning, and diversity and inclusion, or invent new ones that address the evolving needs of our diverse employee base.We are looking for a self-driven Economist to help shape analytics and research roadmap and enable data-driven innovation that fuel our rapidly scaling talent management mission. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at GTM will be expected to develop new techniques to process large data sets, apply a causal lens to the framework, address ambiguous business problems, and contribute to design of automated systems around the company.You will partner closely with product and program owners, as well as scientists and engineers from other disciplines (e.g. data science, software engineers, data engineering) with a clear path to business impact. You develop innovative and even frighteningly bold plans and ideas to discover new ways to advance our goals. You will be expected to be a thought leader as we chart new courses with our rapidly growing employee populations, and lead the way in experimenting new ideas that have not yet been explored.Key Responsibilities:· Participate in scoping and planning of GTM’s Science roadmap· Uncover drivers, impacts, and key influences on talent outcomes· Build new econometric models to improve existing talent products or those that make the case for new products· Bring a causal lens to questions in human resources employing either experiments or non-experimental approaches· Develop predictive and optimization models for key applications· Navigate a variety of data sources, such as enterprise data, customize surveys, focus groups, and/or external data sources· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives· Work in expert cross-functional teams delivering on demanding projects
US, CA, Virtual Location - California
Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists at Amazon will be expected to work directly with senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.
US, MA, Cambridge
The Alexa Translations team is looking for an experienced Applied Science Manager to build industry-leading technologies in speech translation. Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot and Fire TV. Our team's mission is to enable Alexa to break down language barriers for our customers.As an Applied Science Manager, you will lead a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in speech translation. You will work in a hybrid, fast-paced organization where scientists, engineers and product managers work together to build novel 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 and language technology.We are looking for a leader with strong technical experience, demonstrated progression of management scope, and a passion for managing Science talent in a fast-paced environment. In addition to technical depth, you must possess exceptional project management and communication skills, and understand how to coach a team. As a Science leader you will:· Manage and mentor other scientists and engineers, review and guide their work, help develop roadmaps for the team and provide coaching for career development· Contribute directly to our growth by hiring smart and motivated Scientists and managers to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need.· Work closely with other teams across Alexa to deliver platform features that require cross-team leadership.· Represent your business and operations to the highest level of leadership within Amazon.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.
US, MA, Boston
Amazon Elastic File System (EFS) https://aws.amazon.com/efs/ is looking for a Data Scientist to dive deep into the vast data generated by a rapidly growing AWS storage service and applying current data analysis methods to produce insights that will improve customer experience, operational effectiveness, and business value. As a member of the Amazon EFS team, you’ll work closely with outstanding engineers and product managers to work hard, have fun, and create the future of cloud storage.Building a High-Performing & Inclusive Team CultureYou should be passionate about working with a world-class team that welcomes, celebrates, and leverages a diverse set of backgrounds and skillsets to deliver results. Driving results is your primary responsibility, and doing so in a way that builds on our inclusive culture is key to our long term success.Work/Life BalanceEFS values work-life balance. On normal days, our entire team is co-located in the Boston office, but we’re also flexible when people occasionally need to work from home. We generally keep core available hours from 10am to 4pm. Some of the team is available earlier and the rest of us work a little later.Energizing and Interesting Technical ProblemsYou will work in partnership with engineers on the team to build and operate large scale systems that move and transform customer volume data and accelerate access to their data. You’ll be working to provide solutions to both internal and external customers and engage deeply with other teams within EFS, S3, EC2, and many other services. It’s humbling and energizing to provide data movement solutions to customers at AWS scale.Mentorship & Career GrowthWe’re committed to the growth and development of every member of EFS, and that includes our engineers. You will have the opportunity to contribute to the culture and direction of the entire EFS org and deliver initiatives that will improve the life of all of our teams.As a Data Scientist on EFS you will be curious and dive deep into performance, business, and operational metrics. You’re excited about designing solutions that scale while also engaging individual customers in understanding their applications. You’re able to think about business opportunities, operational issues, and architectural diagrams in the course of a single conversation. You’re looking for a team of bright, capable engineers to work with directly in implementing your vision while also collaborating with other data scientists across AWS.
US, WA, Seattle
Workforce Staffing (WFS) supports Amazon Operations by hiring the hourly associates that staff our operational buildings. WFS is quickly becoming one of the world’s largest staffing organizations, forecasted to hire over one million hourly associates across North America and the European Union this year alone. Currently, we hire full time, part time, flex time and seasonal hires across Fulfillment Centers, Sort Centers, Amazon Logistics, Whole Foods, Amazon Air, Prime Now, Amazon Fresh, and emerging business lines. Interested in the businesses that Amazon creates and grows? Here’s your opportunity to be a part of this journey.The Workforce Intelligence team was created in 2018 to support the massive growth in scale and scope that WFS has experienced. The team has continued to grow rapidly in order to meet the expanding needs of the business, including: big data and machine learning solutions, innovative approaches to complex HR problems, and data-driven recommendations during a time of rapid change.Here’s where you come in:As a Research Scientist in Workforce Intelligence, your work is focused on research to deeply understand the people that make up our hourly workforce and help others do the same. You understand that even when hiring hundreds of thousands of hourly associates across multiple types of roles and businesses, the experience of each candidate matters.You use your deep expertise in surveys and statistics (regressions, multilevel models, etc.) to define and answer nebulous problems. You use experimental, quasi-experimental, and RCT methods to understand our candidates and influence critical business decisions. You relentlessly obsess over understanding our candidates and lead our survey program that seeks to amplify the voice of our candidates. You work with colleagues across Research, Data Science, Business Intelligence and related teams to enable Amazon find and hire the right candidates for the right roles at an unprecedented scale.This will be a highly visible role across multiple key deliverables for our global organization. If you are passionate and curious about data, obsess over customers, love questioning the status quo, and want to make the world a better place, let’s chat.
US, WA, Seattle
Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.Please visit https://www.amazon.science for more information.At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday life.What you will do: You will lead a team of talented and experienced scientists and engineers that implement solutions for natural language understanding of Alexa Shopping customers: this involves taking the outputs from automated speech recognition (ASR) component and producing a representation of its meaning. Additionally, your team will build Alexa Shopping Automated CX quality metrics and provide analytics. This involves exploring, developing, socializing, and implementing mechanisms for tracking automated CX quality across customer’s journey with Alexa Shopping to improve Alexa Shopping CX. And finally, you will have the satisfaction of being able to look back and say you were a key contributor to something special from its earliest stages. You will be working closely with executive leadership, multiple product managers and leaders from partner teams in Amazon Retail, Alexa, and Speech Recognition teams.What we are looking for: We are looking for a talented Data Science Manager with a strong technical background and solid people management skills to build, manage and develop a highly-talented and experienced data science team. We are seeking leaders that can guide technical and product innovation in the areas of voice experiences, machine learning models and the distributed systems to bring our vision together. Strong judgment and communication skills, long term technical vision, and continuous focus on engineering and operational excellence are essential for the success in this role.
US, VA, Arlington
Amazon’s Talent Assessment team designs and implements groundbreaking hiring solutions for one of the world’s fastest growing companies. We work in a fast-paced, global environment where we must solve complex problems (ranging from research-based to technical) and deliver solutions that have impact.We are seeking personnel selection researchers with a strong foundation in the development of pre-hire selection assessments, traditional and alternative legally defensible assessment validation approaches, research methodology, and data analysis. We are looking for equal parts researchers and consultant/thought leaders who are highly adaptable and continual learners who thrive in a fast paced environment.You will work closely with global teams to design and experiment new hiring solutions that predict success for highly complex roles (technical and non-technical) that have great impact on Amazon globally.What you’ll do:· Lead the tactical development and execution of large scale, highly visible personnel selection research projects· Develop and iterate on experimental research studies to optimize qualitative and quantitative hiring strategies· · Develop and innovate on new pre-hire test assessment design, validation, and implementation· · Partner with internal and external technology teams· Influence executive project sponsors and multiple business and development teams across the company· Drive effective teamwork, communication, and collaboration across multiple stakeholder groups
US, VA, Arlington
Amazon’s Talent Assessment team designs, implements, and optimizes hiring systems for one of the world’s fastest growing companies. We work in a data-focused, global environment solving complex problems with deep thought, large-sample research, and advanced quantitative methods to deliver practical solutions that make all aspects of hiring more fair, accurate, and efficient.We're looking for a curious data scientist interested in working on a multi-disciplinary team of applied scientists, psychologists, data engineers, business analysts, and program managers. In this role, you will apply your modeling skills to bust myths, create insights, and produce recommendations to help Amazon evaluate millions of potential new hires per year. You'll be involved in all phases of research and experiment design and analysis, including defining research questions, designing experiments, identifying data requirements, conducting statistical or machine learning-based modeling, and communicating insights and recommendations. You'll also be expected to continuously learn new systems, tools, and industry best practices to analyze big data and enhance our analytics.
LU, Luxembourg
Have you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems?If so, we look forward to hearing from you!Amazon Transportation Services is seeking a Research Scientist to be based in the EU Headquarters in Luxembourg. As a key member of the Research Science Team of ATS operations, this person will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon Transportation Network. The successful applicant will ensure that our end-to-end strategies in terms of customer demand fulfillment, routing, consolidation locations, linehaul/airhaul/sea options and last-mile transportation are streamlined and optimized.We welcome candidates with different seniority levels, and the role will be adjusted to candidate’s experience.Tasks/ Responsibilities· Design and prototype algorithmic solutions for standardized processes.· Lead complex time-bound, long-term as well as ad-hoc transportation modelling analyses to help management in decision making.· Communicate to leadership results from business analysis, strategies and tactics (for senior candidates).· Drive large-scale projects to scale and enhance Amazon’s EU transportation network (for senior candidates).· Partner with the planning, linehaul/airhaul and sort center operations teams, while working closely with last-mile, supply chain, and global delivery departments for modeling and optimizing the transportation network of EU.
LU, Luxembourg
Have you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems?If so, we look forward to hearing from you!Amazon Transportation Services is seeking a Research Scientist to be based in the EU Headquarters in Luxembourg. As a key member of the Research Science Team of ATS operations, this person will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon Transportation Network. The successful applicant will ensure that our end-to-end strategies in terms of customer demand fulfillment, routing, consolidation locations, linehaul/airhaul/sea options and last-mile transportation are streamlined and optimized.We welcome candidates with different seniority levels, and the role will be adjusted to candidate’s experience.Tasks/ Responsibilities· Design and prototype algorithmic solutions for standardized processes.· Lead complex time-bound, long-term as well as ad-hoc transportation modelling analyses to help management in decision making.· Communicate to leadership results from business analysis, strategies and tactics (for senior candidates).· Drive large-scale projects to scale and enhance Amazon’s EU transportation network (for senior candidates).· Partner with the planning, linehaul/airhaul and sort center operations teams, while working closely with last-mile, supply chain, and global delivery departments for modeling and optimizing the transportation network of EU.
ES, M, Madrid
Amazon is looking for creative Applied Scientists to tackle some of the most interesting problems on the leading edge of natural language processing (NLP), machine learning (ML), search and related areas with our Amazon Books team. At Amazon Books we believe that books are not only needed to work, education and entertainment, but are also required for a healthy society. As such, we aim to create an unmatched book discovery experience for our customers worldwide. We enable customers to discover new books, authors and genres through sophisticated recommendation engines, smart search tools and through social interaction, and we need your help to keep innovating in this space.If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you. You will develop and implement novel algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, NLP, search, and deep learning. You will innovate, help move the needle for research in these exciting areas and build cutting-edge technologies that enable delightful experiences for hundreds of millions of people.In this role you will:· Work collaboratively with other scientists and developers to design and implement scalable models for accessing and presenting information;· Drive scalable solutions from the business to prototyping, production testing and through engineering directly to production;· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide other members to achieve their career growth potential.
US, WA, Seattle
The Economic Technology team (EconTech, ET) is looking for an Applied Scientist to build Reinforcement Learning solutions to solve economic problems at scale. ET uses Machine Learning, Reinforcement Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building disruptive solutions using cutting-edge technology to solve some of the toughest business problems at Amazon.You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will partner with scientists, economists, and engineers to help invent and implement scalable ML, RL, and econometric models while building tools to help our customers gain and apply insights. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.As an Applied Scientist, you bring structure to ambiguous business problems and use science, logic, and practical experience to decompose them into straightforward, scalable solutions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems; you're interested in learning; and you acquire skills and expertise as needed.
US, WA, Bellevue
Come join the Alexa team, building the speech and language solutions behind Amazon Echo and other Amazon products and services! You will help us invent the future.As a Senior Data Scientist, you will design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, helping Alexa to provide customer great products. This role will be a key member of a Alexa Data Service Science team based in Bellevue, WA. You will work closely with other research scientists, machine learning experts, engineers to design and run experiments, research new algorithms, and find new ways to improve Alexa Data Service products. You will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Our scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.The key responsibility for this role include:· Define proper output business Metrics, and build input models to identify patterns and drivers of the output.· Drive actions at scale using scientifically-based methods and decision making.· Design and develop complex mathematical, statistical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions· Design experiments, test hypotheses, and build actionable models· Prototype these models by using modeling languages such as R or in software languages such as Python.· Work with software engineering teams to drive scalable, real-time implementations· Utilizing Amazon systems and tools to effectively work with terabytes of data
US, WA, Bellevue
Come join the Alexa team, building the speech and language solutions behind Amazon Echo and other Amazon products and services! You will help us invent the future.As a Data Scientist, you will design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, helping Alexa to provide customer great products. This role will be a key member of a Alexa Data Service Science team based in Bellevue, WA. You will work closely with other research scientists, machine learning experts, engineers to design and run experiments, research new algorithms, and find new ways to improve Alexa Data Service products. You will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Our scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.The key responsibility for this role include:· Define proper output business Metrics, and build input models to identify patterns and drivers of the output.· Drive actions at scale using scientifically-based methods and decision making.· Design and develop complex mathematical, statistical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions· Design experiments, test hypotheses, and build actionable models· Prototype these models by using modeling languages such as R or in software languages such as Python.· Work with software engineering teams to drive scalable, real-time implementations· Utilizing Amazon systems and tools to effectively work with terabytes of data
IN, KA, Bangalore
Amazon AI is looking for world class scientists and engineers to join its CodeGuru Reviewer science group. This group is entrusted with developing core program analysis, data mining and machine learning algorithms for Amazon CodeGuru Reviewer. At the Reviewer science group at Amazon AI you will invent, implement, and deploy state of the art program analysis and machine learning algorithms and systems. You will build prototypes and explore conceptually new solutions. You will interact closely with our customers and with the academic community. 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.
DE, BE, Berlin
About us:Amazon is a company of builders. A philosophy of ownership carries through everything we do — from the proprietary technologies we create to the new businesses we launch and grow. You’ll find it in every team across our company; from providing Earth’s biggest selection of products to developing ground-breaking software and devices that change entire industries, Amazon embraces invention and progressive thinking. Amazon is continually evolving; it’s a place where motivated employees thrive, and ownership and accountability lead to meaningful results. It’s as simple as this: we pioneer.With every order made and parcel delivered, customer demand at Amazon is growing. And to meet this demand, and keep our world-class service running smoothly, we're growing our teams across Europe. Delivering hundreds of thousands of products to hundreds of countries worldwide, our Operations teams possess a wide range of skills and experience and this include software developers, data engineers, operations research scientists, and more.About these internships:Whatever your background, if you are excited about modeling huge amounts of data and creating state of the art algorithms to solve real world problems, if you have a passion for using mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software, if you enjoy solving operational challenges by using computer simulations, and if you’re motivated by results and driven enough to achieve them, Amazon is a great place to be. Because it’s only by coming up with new ideas and challenging the status quo that we can continue to be the most customer-centric company on Earth, we’re all about flexibility: we expect you to adapt to changes quickly and we encourage you to try new things.Amazon is looking for ambitious and enthusiastic students to join our unique world as interns. An Amazon EU internship will provide you with an unforgettable experience in a fast-paced, dynamic and international environment; it will boost your resume and will provide a superb introduction to our activities.As an intern in Ops Research and modelling, you could join one of the following teams: Supply Chain, Amazon Logistics, Transportation, Prime Now, Inventory Placement and more.You will put your analytical and technical skills to the test and roll up your sleeves to complete a project that will contribute to improve the functionality and level of service that teams provides to our customers. This could include:· Analyze and solve business problems at their root, stepping back to understand the broader context· Apply advanced statistics and data mining techniques to analyze and make insights from big data (data sets could include: historical production data, volumes, transportation and logistics metrics, simulation/experiment results etc.) in order to forecast, across multiple geographies.· Closely collaborate with operations research scientists, business analysts, BI teams, developers, economists and more on various models’ (including predictive models) development.· Perform quantitative, economic, and/or numerical analysis of the performance of supply chain systems under uncertainty using statistical and optimization tools to find both exact and heuristic solution strategies for optimization problems.· Create computer simulations to support operational decision-making. Identify areas with potential for improvement and work with internal teams to generate requirements that can realize these improvements.· Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies.· Convert statistical output into detailed documents which influence business actions
IN, KA, Bangalore
Are you interested in shaping the future of movies, television, and digital video? Do you want to define what type and quality of X-Ray experiences should be delivered to Amazon customers? Prime Video X-Ray is a service/platform that enables creation and delivery of deep X-Ray experience for any video from any studio for millions of Amazon customers globally. Prime Video X-Ray is an experience that is growing and delighting customers globally on VoD content, Live Sports and Channels. We are looking for a Senior Applied Scientist who can work on different aspects of the video content, like text metadata, video, audio and images to apply from variety of techniques in computer vision, deep learning, machine learning and image processing algorithms to build visual understanding, metadata extraction and curation systems.You will be contributing to a platform from the very early stages which will process terabytes of video content data. You will collaborate with other research scientists across Amazon to define the scope of the product, identify and initiate investigations of new technologies, prototype, test solutions and deliver an exceptional customer experience.You will work closely with the software development teams to build robust vision-based solutions for customer-facing applications. You should be comfortable with a large degree of ambiguity and relish the idea of solving problems that, frankly, haven’t been solved at scale before. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
MX, DIF, Mexico City
At Amazon Web Services (AWS), we’re hiring highly technical Data and Machine Learning engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon SageMaker, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.
MX, DIF, Mexico City
At Amazon Web Services (AWS), we’re hiring highly technical Data and Machine Learning engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon SageMaker, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.