Social
DynamoDB 10-year anniversary Swami Sivasubramanian and Werner Vogels
The early success of the Dynamo database encouraged Swaminathan (Swami) Sivasubramanian (top right), Werner Vogels (lower right) and colleagues to write the Dynamo research paper, and share it at the 2007 ACM Symposium on Operating Systems Principles (SOSP conference). The Dynamo paper served as a catalyst to create the category of distributed database technologies commonly known as NoSQL. Dynamo is the progenitor to Amazon DynamoDB, the company's cloud-based NoSQL database service that launched 10 years ago today.
Cloud and systems

Amazon’s DynamoDB — 10 years later

Amazon DynamoDB was introduced 10 years ago today; one of its key contributors reflects on its origins, and discusses the 'never-ending journey' to make DynamoDB more secure, more available and more performant.

By Staff writer
January 18, 2022
8 min read
Share

Ten years ago, Amazon Web Services (AWS) launched Amazon DynamoDB, a fast, flexible NoSQL database service that offers single-digit millisecond performance at any scale.

In an online post on Jan. 18, 2012, Werner Vogels, chief technical officer at Amazon.com, wrote: “Today is a very exciting day as we release Amazon DynamoDB, a fast, highly reliable and cost-effective NoSQL database service designed for internet scale applications. DynamoDB is the result of 15 years of learning in the areas of large scale non-relational databases and cloud services.

“Several years ago we published a paper on the details of Amazon’s Dynamo technology, which was one of the first non-relational databases developed at Amazon,” Vogels continued. “The original Dynamo design was based on a core set of strong distributed systems principles resulting in an ultra-scalable and highly reliable database system. Amazon DynamoDB, which is a new service, continues to build on these principles, and also builds on our years of experience with running non-relational databases and cloud services, such as Amazon SimpleDB and Amazon S3, at scale. It is very gratifying to see all of our learning and experience become available to our customers in the form of an easy-to-use managed service.”

One of Vogels’s coauthors on the 2007 Dynamo paper, and a key contributor to the development of DynamoDB was Swaminathan (Swami) Sivasubramanian, then an Amazon research engineer working on the design, implementation, and analysis of distributed systems technology, and now vice president of Database, Analytics, and Machine Learning at AWS.

More and more, CIOs and organizations are realizing that it is going to be survival of the most informed, and those that put their data to work are the ones that won't just survive, they will thrive.
Swami Sivasubramanian

A decade after the launch of DynamoDB, Sivasubramanian says we’re “experiencing an amazing era of renaissance when it comes to data and machine learning.”

“We now live in an era where you can actually store your data in these databases and quickly start building your data lakes within Amazon S3 and then analyze them using Amazon SageMaker in a matter of a couple of weeks, if not days. That is simply remarkable.

“We now have the opportunity to help customers gain insights from their data faster,” Sivasubramanian added. “This is a mission that truly excites me because customers really want to put their data to work to enable data-driven decision making. More and more, CIOs and organizations are realizing that it is going to be survival of the most informed, and those that put their data to work are the ones that won't just survive, they will thrive.”

To mark the 10-year anniversary of the launch of Amazon DynamoDB, Amazon Science asked Sivasubramanian three questions about the origins of DynamoDB, its progenitor Dynamo, and the future of DynamoDB.

  1. Q. 

    You were a co-author on the 2007 Dynamo paper. At that time, the industry was transitioning to a scale out vs scale up architectural approach. Can you tell us about the origin story for Dynamo?
    A. 

    To get to 2007, I have to start with 2004, 2005. Even as I was working on my PhD [Sivasubramanian earned his PhD in computer science in 2006 from Vrije Universiteit Amsterdam] I was contemplating where I would work. Ultimately what convinced me to join Amazon as a research engineer intern [2005] was seeing how Amazon was pushing the boundaries of scale.

    I admit I was a little bit of a skeptic as an outsider. At that time, AWS didn’t even exist. But when I joined, I soon had an ‘a ha moment’ that, yes, Amazon was an e-commerce company, but actually it was a technology company that also did e-commerce. It was an interesting revelation for me seeing how Amazon had to invent so many new technologies to even support its e-commerce workload.

    As an intern, I was working as an engineer on amazon.com and during our peak holiday traffic time we experienced a serious scaling failure due to a database transaction deadlocking issue. The problem was caused by the relational database from a commercial vendor that we were using at the time. A bunch of engineers got together and wrote what we call a COE, a correction of errors document in which we say what happened, what we learned, how we fixed the issue, and how we would avoid a recurrence.

    I don't know if it was me being naive or just being confident in the way only a 20 something intern can be, but I asked the question ‘Why are we using a relational database for this? These workloads don't need the SQL level of complexity and transactional guarantees.’

    Peter Vosshall presents Dynamo at 2007 ACM Symposium on Operating System Principles (SOSP).

    This led us to start rethinking how we architected our underlying data stores altogether. At the time there wasn’t a scalable non-relational database. This is what led us to build the original Dynamo, and which led us to write the paper. Dynamo was not the only thing we were rethinking about our architecture at this time. We realized we also needed a scalable storage system, which led us to build S3, and we also realized that we needed a more managed relational database with the ability to do automated replication, failover, and backups/restore, which led us to build Amazon RDS.

    One rule we had related to writing the original Dynamo paper was not to publish when we developed the original design, but first let Dynamo run in production supporting several Amazon.com services, so that the Dynamo paper would be an end-to-end experience paper. Werner and I felt very strongly about this because we didn't want it to be just another academic paper. That’s why I was very proud when 10 years later that paper was awarded a test of time award.

  2. Q. 

    What’s the origin story for DynamoDB, and how has the technology evolved in the past decade?
    A. 

    The idea behind DynamoDB developed from discussions with customers like Don MacAskill, the CEO of SmugMug and Flickr. More and more companies like Don’s were web-based companies, and the number of users online was exploding. The traditional relational database model of storing all the data in a single box was not scaling well. It forced the complexity back on the users to shard their relational databases and then manage all the partitioning and re-partitioning and so forth.

    This wasn’t new to us; these challenges are why we built the original Dynamo, but it wasn’t yet a service. It was a software system that Amazon engineers had to operate. At some point in one of our customer advisory board meetings, Don said, ‘You all started Dynamo and showed what is possible with a scalable non-relational database system. Why can't we have that as an external service?’

    All senior AWS executives were there, and honestly it was a question we were asking ourselves at the time. Don wasn’t the only customer asking for it, more and more customers wanted that kind of scalable database where they didn't have to deal with partitioning and re-partitioning, and they also wanted extreme availability. This led to the genesis of our thinking about what it would take to build a scalable cloud database that wasn’t constrained by the SQL API.

    DynamoDB was different from the original Dynamo because it actually exposed several of the original Dynamo components via very easy-to-use cloud controls. Our customers didn’t have to provision clusters anymore. They could just create a table and seamlessly scale it up and down; they didn’t have to deal with any of the operations, or even install a single library to operate a database. This evolution of Dynamo to DynamoDB was important because we truly embraced the cloud, and its elasticity and scalability in an unprecedented manner.

    Werner Vogel's introduction of DynamoDB on January 18, 2012
    Werner Vogels, vice president and chief technology officer of Amazon.com, introduced DynamoDB on Jan. 18, 2012 with this post in which he said DynamoDB "brings the power of the cloud to the NoSQL database world."

    We launched it on January 18th, 2012 and it was a hit right out of the gate. Don’s company and several others started using it. Right from the launch, not just elasticity, but single-digit latency performance was something that resonated really well with customers. We had innovated quite a bit, all the way from the protocol layer, to the underlying storage layer for SSD storage, and other capabilities that we enabled.

    One of the first production projects was a customer with an interesting use case; they were doing a Super Bowl advertisement. Because DynamoDB was extremely elastic it could seamlessly scale up to 100,000 writes a second, and then scale down after the Super Bowl was over so they wouldn’t incur costs anymore. This was a big deal; it wasn’t considered possible at that time. It seems super obvious now, but at that time databases were not that elastic and scalable.

    It was a bold vision. But DynamoDB’s built-for-the-cloud architecture made all of these scale-out use cases possible, and that is one of the reasons why DynamoDB now powers multiple high-traffic Amazon sites and systems including Alexa, Amazon.com, and all Amazon fulfillment centers. Last year, over the course of our 66-hour Prime Day, these sources made trillions of API calls and DynamoDB maintained high availability with single-digit millisecond performance, peaking at 89.2 million requests per second.

    And since 2012, we have added so many innovations, not just for its underlying availability, durability, security and scale, but ease-of-use features as well.

    Swami Sivasubramanian, AWS | CUBE Conversation, January 2022

    We’ve gone beyond key value store and now support not just a hash-based partition but also range-based partitioning, and we’ve added support for secondary indexes to enable more complex query capabilities —without compromising on scale or availability.

    We also now support scalable change data capture through Amazon Kinesis Data Steams for DynamoDB. One of the things I strongly believe with any database is that it should not be an island; it can’t be a dead end. It should generate streams of what data changed and then use that to bridge it to your analytics applications, or other data stores.

    We have continued innovating across the board on features like backup and restore. For a large-scale database system like DynamoDB with millions of partitions, doing backup and restore isn’t easy, and a lot of great innovations went into making this experience easy for customers.

    We have also added the ability to do global tables so customers can operate across multiple regions. And then we added the ability to do transactions with DynamoDB, all with an eye on how do you continue to keep DynamoDB’s mission around availability and scalability?

    Recently we also launched the ability to reduce the cost of storage with the Amazon DynamoDB Standard Infrequent Access table class. Customers often need to store data long term, and while this older data may be accessed infrequently, it must remain highly available. For example, end users of social media apps rarely access older posts and uploaded images, but the app must ensure that these artifacts are immediately accessible when requested. This infrequently accessed data can represent significant storage expense for customers due to their growing volume and the relatively high cost of storing this data, so customers optimize costs in these cases by writing code to move older, less frequently accessed data from DynamoDB to lower cost storage alternatives like Amazon S3. So at the most recent re:Invent we launched Amazon DynamoDB Standard-Infrequent Access table class, a new cost-efficient table class to store infrequently accessed data, yet maintain the high availability and performance of DynamoDB.

    We are on this journey of maintaining the original vision of DynamoDB as the guiding light, but continue to innovate to help customers with use cases around ease of querying, the ability to do complex, global transaction replication, while also continuing to manage costs.

  3. Q. 

    What might the next 10 years bring?
    A. 

    When we started with DynamoDB ten years ago, the cloud itself was something customers were just starting to understand better — its benefits and what they could do.

    Now we live in a world where cloud is the new normal in terms of how customers are building IT applications, and scale is also the new normal because every app is being built to handle viral moments. DynamoDB itself will be on this continuous journey where we will continue to innovate on behalf of customers. One of the things we will continue moving toward is an end-to-end data strategy mission because, as I mentioned earlier, no database is an island.

    Customers no longer want to just store and query the data in their databases. They then want to analyze that data to create value, whether that’s a better personalization or recommendation engine, or a forecasting system that you can run predictive analytics against using machine learning. Connecting the dots end to end, and continuing to make DynamoDB more secure, more available, more performant, and easier to use will be our never-ending journey.

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Research Scientist, Pricing Science
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes, and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science team builds the machine learning (ML) systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value (LTV) frameworks, and the experimentation infrastructure that validates whether pricing changes actually work. We're looking for a Research Scientist to own causal inference at the intersection of ML and pricing experimentation. Our team has identified a real gap: the methodological bridge between econometric analysis and production-scale ML pipelines needs a practitioner who lives in both worlds. You'll build conditional average treatment effect (CATE) estimation models, design analysis workflows for pricing experiments, and develop reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. If you're a scientist who wants to tackle hard causal identification problems in a high-stakes production environment, and who finds satisfaction in making rigorous methods accessible to a broader team, this role is for you. Key job responsibilities - Design, train, evaluate, and deploy end-to-end causal ML pipelines for pricing use cases, including CATE estimation models and heterogeneous treatment effect frameworks. - Serve as the team's causal ML methodology expert — defining identification strategies, model selection criteria, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. - Contribute causal analysis methodology to pricing A/B test post-analysis and build reusable tooling that economists and other non-ML scientists can adopt without requiring deep ML expertise. - Assess emerging causal inference methods (e.g., synthetic difference-in-differences, generalized random forests, causal representation learning) for applicability to Amazon's pricing context, and author internal methodology proposals for adoption. - Partner with senior economists on identification strategy and causal assumptions, collaborate with software and data engineering partners on production deployment, and align with product managers on experiment design requirements. A day in the life Your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given week you might investigate an optimization anomaly in simulation and trace it to an unmodeled market dynamic, then design an offline evaluation framework to benchmark competing approaches before committing to online testing. You'll collaborate with senior economists on identification strategy for a pricing experiment, present a science proposal for incorporating a new competitiveness signal into an optimization system, or work with the experimentation platform team on randomization design. You'll also have opportunities to write up novel scientific findings for submission to venues such as KDD, NeurIPS, or the ACM Conference on Economics and Computation. About the team The P2 Optimization Science team sits at the center of Amazon's pricing decisions. We build the ML systems, causal inference pipelines, and experimentation infrastructure that determine how prices are set across Amazon's global catalog. Our team brings together research scientists, economists, and engineers who collaborate closely to turn rigorous methodology into production-quality systems with measurable business impact. We value scientific rigor, clear communication across disciplines, and a culture where every team member's work is designed to be extensible and useful beyond their own project. If you want your science to directly influence how hundreds of millions of customers experience Amazon, this is the team to join.
Applied Scientist, Center for Quantum Computing
US, CA, San Francisco
Amazon’s Center for Quantum Computing (CQC) is building the next generation of quantum computing hardware. The Center for Quantum Computing (CQC) is our longest-horizon bet and operates at the intersection of hardware and physics. The goal is a quantum computer that could solve in hours what today's most robust machines would take thousands of years to compute, unlocking breakthroughs in myriad of areas ranging from advanced materials for batteries to cryptography and fundamentally-secure data sharing protocols. This program involves R&D in device theory, materials science, nano-scale fabrication, superconducting circuit design, cryogenic refrigeration, and quantum algorithms and applications. Currently, the quantum hardware team is focused on improving the underlying chip fabrication of the quantum processor and the scaling of the control system needed to run large-scale quantum circuits. As part of this effort, the team is expanding its chip fabrication capabilities through the construction of a new cleanroom facility dedicated to superconducting quantum chip fabrication, and working with strategic partners to develop scaled versions of the control electronics, I/O wiring, and cryogenic refrigeratation needed for utility-scale quantum computers. We operate with a startup-like pace inside one of the world's most capable technology organizations, combining the urgency of a competitive frontier with the resources and infrastructure of pan Amazon. We are seeking an Applied Scientist III to join our team and drive the design, construction, and operation of quantum processors. You will work alongside world-class physicists and engineers to push the frontier of quantum computing — from laser system design and atom trapping to high-fidelity gate execution and quantum error correction. This is a rare opportunity to be part of shaping a technology that could define the next era of computing. Key job responsibilities - Design, build, and operate quantum computing experiments, including optical trapping arrays, laser cooling and state-preparation systems, and high-fidelity gate-driving optics - Develop and optimize two-qubit entangling gates using interactions, targeting fidelities at or beyond the current state of the art (>99.5%) - Implement and characterize erasure-detection protocols leveraging the metastable-state structure of ¹⁷¹Yb to achieve hardware-native erasure conversion rates >90% - Contribute to quantum error correction experiments, including surface codes and quantum low-density parity-check (qLDPC) codes, and work toward demonstrating error suppression factors (Λ) well beyond current published records - Design and improve atom-array loading, rearrangement, and continuous replenishment techniques to support registers of thousands to tens of thousands of atoms - Develop control software and calibration routines for high-speed experimental sequences, including real-time feedback and adaptive protocols - Analyze experimental data, build physical models to explain observed behavior, and propose and execute improvements to system performance - Collaborate with theory, software, and hardware engineering teams to co-design quantum architectures, error-correcting codes, and decoder systems - Publish results in leading scientific journals and present at top conferences to maintain Amazon's visibility in the quantum research community
Imitation Learning Engineer, RIVR
CH, Zurich
RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one. Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies. By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills. We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics. Key job responsibilities Develop imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks. Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms. Streamline the workflow to efficiently expand the imitation learning dataset with new tasks. Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data. Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints. Build, lead and mentor an exceptional team of software engineers. Provide expert guidance to product managers and executives for strategic decision-making. Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.
Principal Applied Scientist, Ads Measurement Science
US, NY, New York
The Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) organization of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. This Principal Applied Scientist leads the technical vision and execution for Amazon Advertising's performance measurement. Our measurement products and tools help customers understand the holistic impact of their spend on Amazon Ads and optimize their portfolios. This is a complex, multi-disciplinary scientific space spanning machine learning, causal inference, statistics and experimentation, natural language processing, and generative AI. As a GenAI-first organization, we build foundational and agentic models that power advertiser use cases across Ads, while empowering our Applied Scientists to directly build and ship products. You will guide a team of scientists with expertise across different domains to come together and innovate on some of the hardest problems in advertising, made more challenging by evolving privacy regulations and technological changes. In measuring the comprehensive impact of advertising, key challenges you will tackle include capturing impact: 1) both for identified and increasingly unidentified web traffic in light of an evolving global privacy landscape; 2) for outcomes across retailers, including both within and beyond Amazon; and 3) for both short- and long-term impacts of advertising. Key job responsibilities * Driving the scientific vision of the teams in your organization and you advise and influence its technical leadership on performance measurement models and products. * Identifying, tackling, and proposing innovative solutions to intrinsically hard, previously unsolved problems. You solve problems pragmatically, applying judgment and experience to balance trade-offs between competing interests. * Bringing clarity to complex problems, probing assumptions, illuminating pitfalls, fostering shared understanding, and guiding towards effective solutions. * Serving and being recognized by internal and external peers as a thought leader in modeled measurement in Ads. You will engage routinely with the broader scientific community, e.g. as a reviewer, program committee member, workshop organizer, or chair at peer-reviewed internal/external conferences. * Leading by example, with your papers, analyses, and code setting the standard in your organization for scientific engineering excellence. * Leading science and design reviews, aligning teams across your organization around coherent algorithmic strategies. * Influencing your team’s science and business strategy by driving one or more team roadmaps contributing to the organization’s roadmap and taking responsibility for some organizational goals. You drive multiple new product features from inception to production launch. * Guiding the career development of others, actively mentoring and educating the larger applied science community on trends, technologies, and best practices. You drive the documentation of scientific innovation. You attract bar-raising talent from academia and industry. * Staying abreast of academic and industry trends, knowing the state of the art, and building on top of existing solutions instead of reinventing when possible. About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
Robotics Applied Scientist
US, CA, Sunnyvale
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building robotic systems that combine frontier AI, sophisticated control, and advanced mechanical design to create adaptable automation capable of working safely alongside humans in dynamic environments. This is an opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. We are seeking an Applied Scientist to develop tactile- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning. To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from simulation onto real hardware. That means being comfortable working against real physical constraints and treating sim-to-real as a core research problem rather than optimizing for benchmark numbers alone. You will work across tactile signal processing, contact-rich manipulation, sim-to-real transfer, and multi-modal sensor fusion to build robust solutions for autonomous grasping, dexterous re-orientation, and fine motor control. You will bring the desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team. Key job responsibilities - Design and train manipulation policies that leverage tactile and force feedback for dexterous grasping, in-hand manipulation, and contact-rich tasks, using reinforcement learning, imitation learning, or hybrid approaches. - Drive sim-to-real transfer, closing the gap between simulated and physical performance on real robotic hands. - Shape objectives and control strategies with the hardware in mind, accounting for how motors and actuators actually behave: transmission ratios, torque versus power, backdrivability, and thermal limits. - Build simulation-based and on-robot evaluation frameworks, with benchmarks and metrics that make tactile perception and policy performance systematically comparable across iterations. - Own scientific and technical projects within the tactile sensing and manipulation workstreams, driving from research concept through deployment on physical robotic systems — and, at the senior level, set direction across a workstream. - Collaborate with hardware, mechanical design, firmware, and controls teams — and with partner applied science organizations — to translate research advances into deployable robotic capabilities, and to inform sensor and actuator design decisions with policy-level requirements. - Publish at top-tier venues and build collaborations with the external research community. - Contribute to a strong scientific bar on the team through code and design review, and mentor engineers and interns working on real-world manipulation and sensing problems. A day in the life Your morning might start with a standup alongside hardware and controls engineers reviewing overnight sim-to-real training runs on a multi-finger gripper, debugging why a grasp policy that worked in simulation is slipping on a real sensor array. Mid-morning you join the weekly tactile sensing workstream to align on sensor integration milestones and share early results from a contact-state estimator you have been prototyping. After lunch you spend a focused block iterating on a reinforcement learning reward formulation, testing variations in simulation before queuing runs on the cluster. Later you pair with a mechanical engineer to review sensor placement trade-offs on an upcoming gripper revision, then wrap the day by drafting a short experiment write-up and sketching next steps for a conference submission. No two days look exactly the same: one week you may be collecting teleoperated demonstrations on the physical robot to seed an imitation learning pipeline; the next you could be deep in a codebase refactor to support a new tactile modality. Throughout, you balance hands-on research — writing algorithms, running experiments, analyzing data — with cross-team collaboration and mentoring. The common thread is moving from research insight to working capability on real hardware, with a tight feedback loop between simulation and the physical world. About the team Our is developing next-generation manipulation capabilities for highly dexterous multi-fingered grippers and hands. Our work spans tactile sensor development, manipulation policy learning, and the tight integration of sensing hardware with intelligent control software. We are building systems that can feel, adapt, and manipulate with human-level dexterity. You will join a team working at the frontier of tactile sensing and contact-rich manipulation, helping define how robots perceive and interact with objects through touch. Because we build the hands as well as the policies that drive them, you will have a direct line to the sensor, actuator, and mechanism designs your algorithms depend on — and real influence over how they evolve. This is an opportunity to solve hard problems across perception, learning, and physical interaction alongside a team of scientists and engineers building toward real-world deployment.
Senior Applied Scientist, Amazon Leo, Amazon Leo Networking Software
US, WA, Redmond
Amazon Leo is building a low Earth orbit satellite broadband network to deliver fast, affordable connectivity to customers around the world, including those underserved by traditional infrastructure. Operating a live constellation and its ground network at scale means service disruptions are an inevitable reality, and how quickly we detect them, understand them, and prevent them from recurring defines the trust we earn as a connectivity provider. We are seeking a talented Senior Applied Scientist to build the next generation of observability and availability science for the Leo network. Leo already detects outages, notifies affected customers, and restores service. The frontier is prediction and prevention: reasoning over complex network and ground-segment telemetry to autonomously detect anomalies before they reach customers, attribute root cause automatically across a sprawling event surface, and rationalize a large, still-maturing metric set so responders trust what the system tells them. In this role you will work backwards from the needs of customers and operations teams. You will own the scientific direction end to end, partnering closely with network engineers, the Network Operations Center, and the Incident Management team, and the Software and Networking orginization to translate a long-term science vision into concrete research and delivery roadmaps. 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. Key job responsibilities * Own the observability and availability science end to end, from long-term vision to research roadmap to production delivery, working backwards from the needs of customers, the NOC, and network engineering. * Develop time-series and graph-based models with dynamic baselining that detect network and ground-segment anomalies before they cross the High Severity threshold, enabling proactive investigation rather than reactive response. * Reason over complex, multi-system telemetry to attribute cause automatically during live HSEs and LSEs, so Call Leaders and engineers spend time mitigating, not hunting. * Provide the science behind the network's bucketed availability decomposition (geometric and satellite coverage, ground and network segments, customer terminals, payload, and planning), attributing every movement to a true cause rather than noise, so leadership steers on signal and engineering effort lands where it actually moves availability and throughput. * Deliver the models, analyses, and metrics that feed the daily system readiness and validation review consumed by senior leadership and product and engineering, turning availability and throughput data into a defensible, evidence-based read on progress against GA entry criteria. * Own the recurring, fleet-wide "hot topic" investigations that span ASIC, firmware, payload, network, and planning, separating correlated symptoms from true root cause across thousands of nodes, quantifying how much availability or throughput each is costing, and answering whether it is a launch-blocking risk and whether it is improving. * Deliver alert correlation and deduplication, false-positive reduction, and metric-quality scoring so responders trust what the system tells them, directly supporting a large, still-maturing KPI set. * Tie your science to the unit the org actually measures: reduce detection and attribution time, raise availability against the network's bar, and increase the share of events prevented before customer impact. * Partner day to day with the Network Operations Center, Network Engineering, and the Incident Management (Call Leader) program; sit on live HSE/LSE bridges as the science partner and turn event learnings into model improvements. * Feed COEs and RCAs back into detection and attribution so recurring signatures are caught earlier, and help design and score gamedays that stress-test both the network and your models against real, closed events. * Build and deploy production pipelines on AWS (e.g., SageMaker, Bedrock, Glue) with model validation, monitoring, drift detection, and explainability, because operations teams must be able to trust and act on model output under pressure. * Set data-science standards, mentor scientists, and drive best practices across the Leo networking organization as the science function scales. * Produce explainable output and written narratives that let responders and leaders three or more levels up act on your models during and between events.
Data Scientist, OTS - Data ANCHOR Team
US, TX, Austin
The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Data Scientist to help own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, supporting proactive incident prevention - Apply econometric and statistical methods to measure the impact of operational interventions and inform resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Contribute to data quality monitoring frameworks and help maintain standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate analytical insights into actionable recommendations - Communicate findings, model performance, and recommendations to stakeholders and senior leaders through written documents and presentations - Contribute to the team's science roadmap, identifying opportunities to apply ML/AI (including Generative AI) to operational challenges A day in the life Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan
Data Scientist, WWGS SSG Strategic Initiative Development
CN, 31, Shanghai
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands. Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact. The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases. Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
2027 Applied Science Internship - Robotics - United States
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
Principal Applied Scientist, FAIM (Full Funnel Agentic Intelligence and Models)
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social