Cryptographic computing can accelerate the adoption of cloud computing

Amazon Scholar Joan Feigenbaum talks about two cryptographic techniques that are being used to address cloud-computing privacy concerns and accelerate enterprise cloud adoption.

  1. Joan Feigenbaum
    Joan Feigenbaum, Amazon Scholar

    Joan Feigenbaum is an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale. In this article, Feigenbaum talks about secure multiparty computation (MPC) and privacy-preserving machine learning (PPML) – two cryptographic techniques that are being used to address cloud-computing privacy concerns and accelerate enterprise cloud adoption.

    According to a 2019 report released by Cybersecurity Insiders, security risks—including the loss or leakage of information—are leading factors that discourage enterprises and government organizations from adopting cloud-computing technologies. As organizations accelerate the flow of sensitive consumer information to the cloud in order to take advantage of its massive compute power, the research area of cryptographic computing is growing in importance.

    At its essence, cryptographic computing focuses on the design and implementation of protocols for using information without revealing it. For example, a county government looking to prioritize the rollout of services based on different areas’ demographics could calculate the average age of residents in different zip codes without running the risk of revealing (indeed without even learning) the ages of individual residents.

    Cryptographic computing is not a new field. In fact, Gentry’s breakthrough scheme for fully homomorphic encryption (FHE) was published as far back as 2008.

    In one of its extensively studied forms, FHE gives each user a public key and a corresponding private key. A user can encrypt any input data set using the public key, give the encrypted input to another party (say a cloud-computing service) that performs computations on it, and then decrypt the results of those computations with her secret key. By ensuring that all data are operated on only in an encrypted state, FHE ensures that data uploaded to the cloud remain confidential. Unfortunately, FHE is not yet fast enough for use on very large-scale data sets.

    That said, there are more narrowly tailored cryptographic-computing techniques that scale better and have started to see commercial use.

  2. Secure multi-party computation (MPC)

    Secure multi-party computation (MPC) enables n parties P1,...,Pn, with private inputs x1,...,xn, to compute y = f(x1,...,xn) in such a way that all parties learn y but no Pi learns anything about xj, for ji, except what is logically implied by y and xi.

    Consider the following toy example. Suppose 20 pupils, whom we will call P1 through P20, are in the same class and have received their graded exams from their teacher. They want to compute the average of their grades without revealing their individual grades, which we will denote by g1 through g20. They can use the following simple MPC protocol. P1 chooses a random number r, computes x1 = g1 + r, and sends x1 to P2. Then P2 computes x2 = x1 + g2 and sends x2 to P3. They continue in this fashion until P20 computes x20 = x19 + g20 and sends x20 to P1. In the last step, P1 computes x20 – r, which is of course the sum g1 + g2 + … + g20 of the individual grades. He divides this sum by 20 to obtain the average and broadcasts the result to all of the pupils.

    If all of the pupils follow this protocol faithfully, then they all learn the average, but none learns anything about the others’ grades except what is logically implied by the average and his own grade. Here, “following the protocol faithfully” requires not colluding with another pupil to discover someone else’s grade. If, say, P3 and P5 executed all of the steps of the protocol correctly but also got together on the side to pool their information, they could compute P4’s grade g4. That is because g4 = x4 – x3, and, during the execution of the protocol, P3 learns x3 and P5 learns x4. Fortunately, there are techniques (the details of which are beyond the scope of this article) for ensuring that this type of collusion does not reveal private inputs; they include secret-sharing schemes, described below.

    One powerful class of MPC protocols proceeds in multiple rounds. In the first round, each Pi breaks xi into shares, using a secret-sharing scheme, and sends one share to each Pj. The information-theoretic properties of secret sharing guarantee that no other party (or even limited-sized coalition of other parties) can compute xi from the share(s). The parties then execute a multi-round protocol to compute shares of y, in which the shares of intermediate results computed in each round also do not reveal xi. In the last round, the parties broadcast their shares of y so that all of them can reconstruct the result.

    In the secure-outsourcing protocol architecture, depicted below, the parties P1,...,Pn play the role of input providers and a disjoint, much smaller set of parties S1,...,Sk play the role of secure-computation servers; typically, 2 ≤ k ≤ 4. The input providers share their inputs with the servers, which then execute a basic, k-party MPC protocol to compute y. For an appropriate choice of secret-sharing scheme, the inputs remain private as long as at least one server does not collude with the others. Note that cloud-computing companies are ideally positioned to supply secure computation servers!

    MPC.JPG
    The Secure-Outsourcing Architecture with n=8 and k=4
    Image credit: Joan Feigenbaum

  3. Privacy-preserving machine learning (PPML)

    An ML training algorithm is given a set of solved instances of a classification problem and produces a model to be used by an ML prediction algorithm to classify future, as-yet-unsolved instances of the same problem.

    Training data, queries (inputs to the prediction algorithm), and predictions (outputs of the prediction algorithm) may contain sensitive information about data subjects. Owners of commercially valuable models regard them as intellectual property and may wish to sell access to them but not permit users to reverse-engineer them. Privacy-preserving machine learning (PPML) is the subarea of cryptographic computing that studies algorithms that protect training data, models, queries, and predictions.

    Practical PPML methods are often tailored for specific training or prediction algorithms and may require specific computational architectures. The cloud provider can employ both traditional computer-security techniques (authentication, sandboxing, etc.) and PPML algorithms to protect both sensitive data and intellectual property. For example, the 2019 PPML annual workshop focused on MPC, FHE, and other techniques outlined in this article. In addition, the workshop featured recent results on differential privacy, a powerful data-protection approach that has gained a lot of attention in recent years. Differential privacy enables users to obtain aggregate information from a database while protecting confidential information about individual records in the database. Indeed, the result of a differentially private statistical query is not significantly affected by the presence or absence of any particular individual record.

    PPMLSchema.JPG
    Image credit: Joan Feigenbaum and Xianrui Meng

    Secure, multi-party computation and privacy-preserving machine learning are only two cryptographic-computing techniques that are candidates for widespread practical deployment. Other techniques include searchable encryption, which enables keyword search on encrypted documents, garbled-circuit protocols, which are a form of secure, two-party computation, and protocols for queries to encrypted databases.

    I’m personally excited to see these innovations in cryptographic computing, which will be critical to easing contractual and regulatory barriers to adoption of cloud computing and could herald an era of even stronger growth for the industry. Cryptographic computing will allow individuals around the globe to reap the benefits of cloud computing, such as personalized medicine, movie streaming, and smarter financial-management solutions, while ensuring that our personal information stays private and secure.


View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Sign up for our monthly newsletter

Work with us

See More Jobs
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity.Come work on the Prime Air team!We're looking for an outstanding Research Scientist who combines superb technical, research and analytical capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams.In this role you will develop, implement and test controls for the Prime Air drone. The ideal candidate will have fundamental knowledge of simulation, dynamics, aerodynamics, design and analysis with some practical real-life implementation experience.Export Control LicenseThis position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on ’s ability to apply for and obtain an export license on your behalf.
US, WA, Seattle
At Amazon, we strive every day to be Earth’s most customer centric company. Do you want to join an innovative team who uses traditional machine learning, deep learning, and natural language processing techniques to insert intelligence into our processes to help provide world class support to our global network of selling partners in an efficient and scalable manner? Are you interested in helping our associates by streamlining their processes and offering them fast, efficient routes and tools to case resolution? Amazon Partner Solutions And Support Machine Learning team is looking for an Applied Scientist to build efficient, flexible, and scalable machine learning and general applied science solutions that help us solve our most challenging problems. In this role, you will have ownership of the end-to-end development of solutions to complex problems and you’ll play an integral role in strategic decision-making. You will also work closely with engineers to build ML pipelines, platforms and solutions that solve problems of intent classification, automation, and workforce optimization.
US, WA, Seattle
Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals.Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from harmful content ? Do you want to build advanced algorithmic systems that help millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join our Amazon Prime Video team.We are expanding our scene understanding team to drive compliance automation and exceptional customer experience using machine learning, computer vision, audio processing, and natural language understanding. Automation of video understanding at scale is our mission and passion. We need to solve problems across many cultures and languages. we have a huge amount of human-labelled data, and operation team to generate labels across many languages. Our team innovates, with many novel patents, inventions, and papers in the motion picture and television industry. We are highly motivated to extend the state of the art.As an applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. This is a greenfield with no "off-the-shelf algorithms" that can perform the job. We experiment a lot and it is a must to learn and be curios. You will be encouraged to see the big picture, be innovative, and positively impact millions of customersYou'll work with experienced managers who'll care for you. We'll guide you on your career growth path and there's no shortage of technical challenges.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions.We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.
US, CA, Hawthorne
We are looking for an experienced and motivated Data Scientist with proven abilities to build and manage modeling projects, identify data requirements as well as build methodology and tools that are statistically grounded.You should be an expert in the areas of data science, optimization, machine learning and statistics, and are comfortable facilitating ideation and working from concept through execution. As a member of the Ring Failure Analysis team, your primary responsibilities include supporting investigations to determine root cause of failures in consumer electronics. You will work with other subject matter experts in video, motion sensing, power and communication technologies to help improve products and the customer experience. You will be required to multi-task and prioritize work to meet schedule and cost needs.The ideal candidate should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. This role requires a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.RESPONSIBILITIES· Responsible for identifying and researching failures at the system level to improve product yield, quality and reliability.· Work with functional teams as needed to understand device issues.· Articulate conclusions to the team and advocate for appropriate action.· Develop new diagnostic tools or tests and more efficient FA techniques.· Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL· Interface with business customers, gathering requirements and delivering complete data structures· Drive well-formed experiment design and measurement plans.· Monitor existing processes, create and automate new and existing reporting and work across the organization to make actionable decisions available to stakeholders.· Assist with ongoing FA investigations by providing metrics as well as statistical analysis.
US, CA, San Diego
At Amazon, we strive every day to be Earth’s most customer centric company. Amazon Perfect Order Experience (POE) team works to ensure that customers can buy with confidence on Amazon. We develop and implement large scale machine learning solutions to protect the buying experience on Amazon while minimizing friction for our selling partners. We develop state-of-the art Natural Language Processing models to detect negative customer experience in real-time and build an ever-evolving risk monitoring system to proactive protect customer trust.We are looking for a Sr. Applied Scientist to build efficient and scalable machine learning systems to keep amazon the safest and most trusted place to shop online. In this role, you will drive innovation and lead critical scientific projects. You will work closely with scientists, economists and engineers to build end-to-end ML solutions that have immediate impacts on amazon customers. You will work on a variety of research areas including:· Develop NLP and deep learning models to extract insights from customer feedback.· Build the next generation of risk monitoring system using predictive modeling, graph mining and unsupervised learning techniques.· Apply the state-of-the art Computer Vision technique to develop a highly scalable ML solution for product authentication.· Develop and deploy real-time ML models using AWS services.
US, CA, San Diego
At Amazon, we strive every day to be Earth’s most customer centric company. Amazon Perfect Order Experience (POE) team works to ensure that customers can buy with confidence on Amazon. We develop and implement large scale machine learning solutions to protect the buying experience on Amazon while minimizing friction for our selling partners. We develop state-of-the art Natural Language Processing models to detect negative customer experience in real-time and build an ever-evolving risk monitoring system to proactive protect customer trust.We are looking for an Applied Scientist to build efficient and scalable machine learning systems to keep amazon the safest and most trusted place to shop online. In this role, you will work closely with scientists, economists and engineers to build end-to-end ML solutions that have immediate impacts on amazon customers. You will work on a variety of research areas including:· Develop NLP and deep learning models to extract insights from customer feedback.· Build the next generation of risk monitoring system using predictive modeling, graph mining and unsupervised learning techniques.· Apply the state-of-the art Computer Vision technique to develop a highly scalable ML solution for product authentication.· Develop and deploy real-time ML models using AWS services.
US, CA, San Diego
At Amazon, we strive every day to be Earth’s most customer centric company. Amazon Perfect Order Experience (POE) team works to ensure that customers can buy with confidence on Amazon. We develop and implement large scale machine learning solutions to protect the buying experience on Amazon while minimizing friction for our selling partners. We develop state-of-the art Natural Language Processing models to detect negative customer experience in real-time and build an ever-evolving risk monitoring system to proactive protect customer trust.We are looking for an Applied Scientist to build efficient and scalable machine learning systems to keep amazon the safest and most trusted place to shop online. In this role, you will work closely with scientists, economists and engineers to build end-to-end ML solutions that have immediate impacts on amazon customers. You will work on a variety of research areas including:· Develop NLP and deep learning models to extract insights from customer feedback.· Build the next generation of risk monitoring system using predictive modeling, graph mining and unsupervised learning techniques.· Apply the state-of-the art Computer Vision technique to develop a highly scalable ML solution for product authentication.· Develop and deploy real-time ML models using AWS services.
US, MA, Metro West
Sr. Applied Scientist - Amazon Physical Stores TechnologiesAmazon is build new technologies to advance physical retails and come join an ambitious project developing new technologies that go well beyond the current state of the art to address an enormous market that will impact the daily lives of tens of millions of people.As a senior applied scientist, you will help solve a variety of technical challenges and mentor other scientists. You will be technical leader in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
US, MA, Metro West
Sr. Applied Scientist - Amazon Physical Stores TechnologiesAmazon is build new technologies to advance physical retails and come join an ambitious project developing new technologies that go well beyond the current state of the art to address an enormous market that will impact the daily lives of tens of millions of people.As a senior applied scientist, you will help solve a variety of technical challenges and mentor other scientists. You will be technical leader in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLEEntity: Amazon Web Services, Inc.Title: Economist IILocation: Seattle, WAPosition Responsibilities:Solve key business problems faced in retail, advertising, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations through application of economic theory. Apply frontier of economic thinking to market design, pricing, forecasting, program evaluation, and online advertising. Build econometric models using data systems. Develop new techniques to process large data sets, address quantitative problems, and contribute to automated systems design. Apply tools from applied micro-econometrics (e.g. experimental design, difference-in-difference, regression discontinuity, and IV) and forecasting (essential time series models). Leverage big data tools for data extraction. Write up and present analysis for distribution to various levels of management at Amazon.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in technology to solve real-world problems. As a Research Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in technology to solve real-world problems. As a Research Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers.
US, WA, Seattle
Would you like to help us build the next-generation cloud services that will power the largest managed infrastructure in the world? We’re hiring an Applied Scientist for AWS Compute Optimizer team within Amazon AWS EC2. Our service uses large amounts of data combined with machine learning (ML) and push the boundaries of scale, availability, and performance, while maintaining the highest standards for security and operational excellence. By applying the knowledge drawn from Amazon’s own experience running diverse workloads in the cloud, Compute Optimizer identifies workload patterns and recommends optimal compute resources. Compute Optimizer analyzes the configuration and resource utilization of your workload to identify dozens of defining characteristics, for example, if a workload is CPU-intensive, or if it exhibits a daily pattern or if a workload accesses local storage frequently. As part of the team, you will help develop a set of next-generation services within our core AWS EC2 products.You will take on challenges in providing right-sizing recommendations based on billions of metric records available for various customers and their use cases. You will be empowered to think big, invent on behalf of our customers, make judgment calls and find elegant solutions to hard problems.Position Responsibilities:· Build machine learning models for AWS Compute Optimizer· Drive collaborative research and creative problem solving· Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.· Design, develop, and implement production level code· Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment.· Collaborate with other engineers and related teams to find technical solutions to complex design problems· Constructively critique peer research and mentor junior scientists and engineersFor more information, please visit https://aws.amazon.com/compute-optimizer/
US, WA, Seattle
Why this job is awesome?· · This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.· · MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.· · We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.· - Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site?- Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?- Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?- Do you like to innovate and simplify?If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.Major responsibilities:· Lead a ML team to research and implement machine learning and statistical techniques to create scalable and effective models in Delivery Experience (DEX) systems· Solve business problems and to identify business opportunities to provide the best delivery experience on all Amazon-owned sites.· Design, development and evaluation of highly innovative machine learning models for big data.· Analyzing and understanding large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities· Working closely with other software engineering teams to drive real-time model implementations and new feature creations· Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
US, MD, Virtual Location - Maryland
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center and non-profit agencies derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a Professional Service office.A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person who likes to have fun, loves to learn, and wants to innovate in the world of AI. Major responsibilities include:· · Understand the customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances .· · Assist customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· · Use Deep Learning frameworks like PyTorch, Tensorflow and MxNet to help our customers build DL models.· · Use SparkML and Amazon Machine Learning (AML) to help our customers build ML models.· · Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.· · Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built.· · Assist customers with identifying model drift and retraining models.· · Research and implement novel ML and DL approaches, including using FPGA.· · This position can have periods of up to 10% travel.· · This position requires that the candidate selected be a US Citizen and obtain and maintain an active TS/SCI security clearance.· Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, WA, Seattle
Amazon Web Services (AWS) is obsessed with ensuring the success of our customers. To this end, AWS is learning how to best train, compensate, and deploy its large and growing global salesforce. Which payment plans lead to good customer outcomes in the long run? What type of training – and how much – helps drive opportunity creation and win rate? What customers would benefit most from the help of an AWS expert? These are the types of questions our team seeks to answer.AWS is hiring an economist specializing in program evaluation / reduced form causal analysis to help estimate the impact of – and then optimize – different compensation, training, and assignment programs. While causal analysis is our bread and butter, we see opportunities for those who have (or are willing to invest in building) broad skillsets. If you have an EIO or forecasting background, we’d love to talk.Job Locations: Seattle, WA
GB, Cambridge
Interested in Amazon Alexa? We’re building the speech and language solutions behind Amazon Echo and other Amazon products and services. Come join us!Amazon is looking for passionate, talented, and inventive Scientists to help build world-leading Speech and Language technology. Our mission is to create a delightful experience to Amazon’s customers by advancing the state of the art in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Machine Learning (ML).As part of our speech and language team, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to build and advance state-of-the-art spoken language understanding systems. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands-on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding.We are hiring in all areas of spoken language understanding with a special focus on acoustic modeling, language modeling, finite state methods, etc.Candidates should have a strong background in machine learning, statistics, and coding, are eager to learn and have a "can do" attitude.As a Research Scientist on our team, you will build, extend and optimize cutting-edge spoken language understanding systems and conduct core research aimed at advancing the state of the art. This involves:· Researching the latest modeling techniques. Understanding trade-offs between competing approaches, and identifying the ones that are likely to have real impact on our customers.· Implementing and improving modeling tools, training recipes and prototypes utilizing programming skills in Python, Java and/or C++.· Conducting experiments to assess the quality of speech recognition and natural language processing models and to study the effectiveness of different modeling techniques.· Analyzing field data in order to identify areas of possible improvement or enhancement of the system.· Presenting and discussing ideas and results within the team and with internal stakeholders.
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world.Come work at Amazon!Innovation is part of our DNA! Our goal is to be Earth’s most customer centric company, and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in distributed systems and hardware design.The Role:You will be part of a team developing high bandwidth systems for free space optical communication between satellites. You will lead characterization of the systems and develop processes for calibration and tests on the ground as well as in space.You will tackle challenging, novel situations every day and have the opportunity to work with multiple technical teams at Amazon. You should be comfortable with a high degree of ambiguity and relish the idea of solving problems that haven't been solved at scale before. Along the way, we guarantee that you will learn a lot, have fun and make a positive impact on millions of people.Export Control Requirement:Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
US, WA, Seattle
Amazon’s High Value Messaging (HVM) Analytics team (part of Customer Behavior Analytics) is looking for a Senior Applied Scientist to spearhead the rapid growth of our Marketing Measurement solutions. The team focuses on building scalable scientific models to estimate the effectiveness of Amazon marketing efforts and provide actionable insights to the various marketing teams within Amazon. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference.A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon marketing, as well as allow you to be part of the large science community within the Customer Behavior Analytics (CBA) organization.The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.The main responsibilities for this position include:· Apply expertise in ML and causal modeling to develop systems that describe how Amazon’s marketing campaigns impact customers’ actions· Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions· Improve upon and simplify our existing solutions and frameworks· Review and audit modeling processes and results for other scientists, both junior and senior· Work with marketing leadership to align our measurement plan with business strategy· Formalize 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· Identify new opportunities that are suggested by the data insights· Bring a department-wide perspective into decision making· Develop and document scientific research to be shared with the greater science community at Amazon
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.