Social

2020 Amazon Research Awards recipients announced

ARA funds nearly twice as many awards as in previous year; 100 award recipients represent 59 universities in 13 countries.

By Amazon Research Awards team
April 27, 2021
11 min read
Share

In March 2021, Amazon notified applicants that they were recipients of the 2020 Amazon Research Awards, a program that provides unrestricted funds and AWS Promotional Credits to academic researchers investigating research topics across a number of disciplines.

Today, we’re publicly announcing the 100 award recipients who represent 59 universities in 13 countries. This round, ARA received a record number of submissions and funded nearly twice as many awards as the previous year. Each award is intended to support the work of one to two graduate students or postdoctoral students for one year, under the supervision of a faculty member.

ARA is funding awards under five call for proposals: AI for Information Security, Alexa Fairness in AI, AWS AI, AWS Automated Reasoning, and Robotics. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community, and society more generally. Theoretical advances, creative new ideas, and practical applications were all considered.

Recipients have access to more than 200 Amazon public datasets, and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

“The 2020 Amazon Research Awards recipients represent a distinguished array of academic researchers who are pursuing research across areas such as ML algorithms and theory, fairness in AI, computer vision, natural language processing, edge computing, and medical research,” said Bratin Saha, vice president of AWS Machine Learning Services. “We are excited by the depth and breadth of their proposals, as well as the opportunity to advance the science through strengthened connections among academic researchers, their institutions, and our research teams.”

“As we enter into this golden age of robotics, we do so with our university partners. Not only are they shaping what is possible in robotics, they are inspiring many next- generation roboticists with their incredible creations and front-line teachings,” said Tye Brady, chief technologist for Amazon Robotics. “Our grant recipients are not only pursuing cutting-edge research that will benefit society, but perhaps more importantly are helping students from across the globe pursue a career in science and engineering.”

ARA funds proposals up to four times a year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

Below is the list of 2020 award recipients, presented in alphabetical order.

Recipient

University

Research title

Vikram Adve

University of Illinois Urbana-Champaign

Extending the LLVM compiler infrastructure for tensor architectures

Pulkit Agrawal

Massachusetts Institute of Technology

A framework for multi-step planning for manipulating rigid objects

Ron Alterovitz

University of North Carolina at Chapel Hill

Cloud-based motion planning: an enabling technology for next-generation autonomous robots

Jimmy Ba

University of Toronto

Model-based reinforcement learning with causal world models

Saurabh Bagchi

Purdue University—West Lafayette

Content and contention-aware approximate streaming video analytics for edge devices

David Baker Effendi

Stellenbosch University

Dataflow analysis using code property graphs, graph databases and synchronized pushdown systems

Sivaraman Balakrishnan

Carnegie Mellon University

Foundations of robust machine learning: from principled approaches to practice

Elias Bareinboim

Columbia University

Off-policy evaluation through causal modeling

Clark Barrett

Stanford University

Model-based testing of SMT solvers

Lars Birkedal

Aarhus University

Modular reasoning about distributed systems: higher-order distributed separation logic

David Blei

Columbia University

New directions in observational causal inference

Eric Bodden

Paderborn University

HybridCG — dynamically-enriched call-Graph generation of Java enterprise applications

Legand Burge

Howard University

Voice-FAQ: artificial intelligence for triaging cognitive decline through modeling vocal prosody and facial expressions

James Caverlee

Texas A&M University, College Station

Fairness in recommendation without demographics

Changyou Chen

University at Buffalo

Scaling up human-action analysis systems

Danqi Chen

Princeton University

Building broad-coverage, structured dense knowledge bases for natural language processing tasks

Helen Chen

University of Waterloo

Optimizing pretrained clinical embeddings for automatic COVID-related ICD coding

Yiran Chen

Duke University

Privacy-preserving representation learning on graphs — a mutual information perspective

Margarita Chli

ETH Zurich

Vision-based emergency landing in urban environments using reinforcement learning and deep learning

Kyunghyun Cho

New York University

Independently controllable attributes for controllable neural text generation

Carlo Ciliberto

University College London

Optimal transport for meta-learning

Loris D’Antoni

University of Wisconsin–Madison

Correct-by-construction IAM policies

David Danks

Carnegie Mellon University

An integrated framework for understanding human-AI hybrid decision-making

Suhas Diggavi

University of California, Los Angeles

Compressed private and secure distributed edge learning

Greg Durrett

University of Texas At Austin

Making conditional text generation fair and factual

Sergio Escalera

Universitat de Barcelona and Computer Vision Center

Portable virtual try-on for smart devices

Jan Faigl

Czech Technical University in Prague

Communication maps building in subterranean environments

Pietro Ferrara

Ca’ Foscari University of Venice

IAM access control policies verification and inference

Katerina Fragkiadaki

Carnegie Mellon University

Generalizing manipulation across objects, configurations and views using a visually-grounded library of behaviors

Guillermo Gallego

Technical University of Berlin

Online in-hand object tracking and grasp failure detection with an event-based camera

Grace Gao

Stanford University

Trustworthy autonomous vehicle localization using a joint model-driven and data-driven approach

Stephanie Gil

Harvard University

Enabling the next generation of coordinated robots: scalable real-time decision making

Luca Giuggioli

University of Bristol

Multi-robot online exploration in extreme unbounded environments through adaptive socio-spatial ordering

Jorge Goncalves

University of Melbourne

Integrated qualification test framework to measure crowd worker quality and assign or recommend heterogeneous tasks

Ananth Grama

Purdue University—West Lafayette

Scaling causal inference to explainable clinical recommendations

Grace Gu

University of California, Berkeley

Surrogate machine learning model and quasi-static simulation of pneumatically actuated robotic devices

Ronghui Gu

Columbia University

Microverification of the Linux KVM hypervisor: proving VM confidentiality and integrity

Aarti Gupta

Princeton University

Learning abstract specifications from distributed program implementations

Saurabh Gupta

University of Illinois Urbana-Champaign

Self-supervised discovery of object states and transitions from unlabeled videos

Daniel Harabor

Monash University

Anytime constraint-based multi-agent pathfinding

Hynek Hermansky

Johns Hopkins University

Multistream lifelong federated learning for machine recognition of speech

Bin Hu

University of Illinois Urbana-Champaign

Provably robust adversarial reinforcement learning for sequential decision making in safety-critical environments

Lifu Huang

Virginia Tech

Event-centric temporal and causal knowledge acquisition and generalization for natural language understanding

Dinesh Jayaraman

University of Pennsylvania

Learning modular dynamics models for plug-and-play visual control

Sven Koenig

University of Southern California

Improving planning and plan execution for warehouse automation

Laura Kovacs

TU Wien

FOREST: first-order reasoning for ensuring system security

Arun Kumar

University of California, San Diego

Improving automated feature type inference for AutoML on tabular data

Himabindu Lakkaraju

Harvard University

Towards reliable and robust model explanations

Kevin Leyton-Brown

University of British Columbia

Automated machine learning for tabular datasets using hyperband embedded reinforcement learning

Bo Li

University of Illinois Urbana-Champaign

Machine learning evaluation as a service for robustness, fairness, and privacy utilities

Ke Li

University of Exeter

Many hands make work light: multi-task deep semantic learning for testing web application firewalls

Zhiqiang Lin

Ohio State University

Type-aware recovery of symbol names in binary code: a machine learning based approach

Jeffrey Liu

Massachusetts Institute of Technology

Integrating the low altitude disaster imagery (LADI) dataset into the MIT Beaver Works curriculum

Michael Mahoney

University of California, Berkeley

Systematic methods for efficient inference and training of neural networks

Radu Marculescu

University of Texas

New directions for 3D object detection: distributed inference on edge devices using knowledge distillation

Ruben Martins

Carnegie Mellon University

Improving performance and trust of MaxSAT solvers

Jiri Matas

Czech Technical University in Prague

Training neural networks on non-differentiable losses

Michael Milford

Queensland University of Technology

Complementarity-aware multi-process fusion for long term localization

Heather Miller

Carnegie Mellon University

Directed automated explicit-state model checking for distributed applications

Ndapa Nakashole

University of California, San Diego

Learning representations for voice-based conversational agents for older adults

Shrikanth Narayanan

University of Southern California

Toward inclusive human-AI conversational experiences for children

Lerrel Pinto

New York University

Learning to manipulate deformable objects through robust simulations

Ravi Ramamoorthi

University of California, San Diego

Sparse multi-view object acquisition using learned volumetric representations

Philip Resnik

University of Maryland, College Park

Advanced topic modeling to support the understanding of COVID-19 and its effects

Daniela Rus

Massachusetts Institute of Technology

Learning to plan through imagined self-play for multi-agent system

Supreeth Shashikumar

University of California, San Diego

Privacy preserving continual learning with applications to critical care

Robert Shepherd

Cornell University

Enduring and adaptive robots via electrochemical blood

Cong Shi

University of Michigan, Ann Arbor

Machine learning for personalized assortment optimization

Florian Shkurti

University of Toronto

Generating physically realizable adversarial driving scenarios via differentiable physics and rendering simulators

Abhinav Shrivastava

University of Maryland, College Park

The pursuit of knowledge: discovering and localizing new concepts using dual memory

Roland Siegwart

ETH Zurich

Safe self-calibration of hybrid aerial vehicles

Sameer Singh

University of California, Irvine

Detecting and fixing vulnerabilities in NLP models via semantic perturbations and tracing data influence

Noah Smith

University of Washington - Seattle

Language model customization

Mahdi Soltanolkotabi

University of Southern California

Artificial intelligence for fast and portable medical imaging (with limited training data)

Seung Woo Son

University of Massachusetts Lowell

Reliable and accurate anomaly detection in edge nodes using sparsity profile

Dawn Song

University of California, Berkeley

Knowledge-enhanced cyber threat hunting

Dezhen Song

Texas A&M University, College Station

Optoacoustic material and structure pretouch sensing at robot fingertip

Shuran Song

Columbia University

Dexterity through diversity:learning a generalizable grasping policy for diverse end-effectors

Yizhou Sun

University of California, Los Angeles

Accelerating graph neural network training

Russ Tedrake

Massachusetts Institute of Technology

Intuitive physics for manipulation

James Tompkin

Brown University

Real-time multi-camera fusion for unoccluded VR robot teleoperation

Emina Torlak

University of Washington - Seattle

Automated verification of JIT compilers for BPF

Marynel Vazquez

Yale University

Evaluating social robot navigation via online human-driven simulations

Nisheeth Vishnoi

Yale University

Fair and error-resilient algorithms for AI and ML

Gang Wang

University of Illinois at Urbana–Champaign

Combating concept drift in security applications via proactive data synthesis

Hao Wang

Rutgers University-New Brunswick

Structured domain adaptation with applications to personalization and forecasting

James Wang

Pennsylvania State University

Affective and social interaction between human and intelligent machine

Gloria Washington

Howard University

Towards identification of uncomfortable speech in conversations

Chuan Wu

The University of Hong Kong

Compilation optimization in distributed DNN training: joining OP and tensor fusion/partition

Eugene Wu

Columbia University

Human-in-the-loop data debugging for ML-oriented analytics

Jiajun Wu

Stanford University

Implicit dynamic scene representation learning for robotics

Ming-Ru Wu

Dana-Farber Cancer Institute

From bench to clinic – machine-learning based cancer immunotherapy design

Diyi Yang

Georgia Institute of Technology

Abstractive conversation summarization at scale

Sixian You

Massachusetts Institute of Technology

AI-driven label-free histology for cancer diagnosis

Jingjin Yu

Rutgers University-New Brunswick

Pushing the limits of efficient and optimal multi-agent path finding through exploring space utilization optimization and adaptive planning horizon heuristics

Rui Zhang

Pennsylvania State University

Building robust conversational question answering systems over databases of tabular data

Yu Zhang

University of South Florida

Design of an automated advanced air mobility flight planning system (AAFPS)

Yuke Zhu

University of Texas at Austin

Learning implicit shape affordance for grasping and manipulation

Marinka Zitnik

Harvard University

Actionable graph learning for finding cures for emerging diseases

James Zou

Stanford University

How to make AI forget you? Efficiently removing individuals’ data from machine learning models
Tags
About the Author
Amazon Research Awards team

Related content

Work with us

See more jobs See more jobs
Applied Scientist , Amazon Customer Service
US, CA, Santa Clara
We are seeking an Applied Scientist II to join Amazon Customer Service's Science team, where you will build AI-based automated customer service solutions using state-of-the-art techniques in retrieval-augmented generation (RAG), agentic AI, and post-training of large language models. You will work at the intersection of research and production, developing intelligent systems that directly impact millions of customers while collaborating with scientists, engineers, and product managers in a fast-paced, innovative environment. Key job responsibilities - Design, develop, and deploy information retrieval systems and RAG pipelines using embedding models, reranking algorithms, and generative models to improve customer service automation - Conduct post-training of large language models using techniques such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO) to optimize model performance for customer service tasks - Build and curate high-quality datasets for model training and evaluation, ensuring data quality and relevance for customer service applications - Design and implement comprehensive evaluation frameworks, including data curation, metrics development, and methods such as LLM-as-a-judge to assess model performance - Develop AI agents for automated customer service, understanding their advantages and common pitfalls, and implementing solutions that balance automation with customer satisfaction - Independently perform research and development with minimal guidance, staying current with the latest advances in machine learning and AI - Collaborate with cross-functional teams including engineering, product management, and operations to translate research into production systems - Publish findings and contribute to the broader scientific community through papers, patents, and open-source contributions - Monitor and improve deployed models based on real-world performance metrics and customer feedback A day in the life As an Applied Scientist II, you will start your day reviewing metrics from deployed models and identifying opportunities for improvement. You might spend your morning experimenting with new post-training techniques to improve model accuracy, then collaborate with engineers to integrate your latest model into production systems. You will participate in design reviews, share your findings with the team, and mentor junior scientists. You will balance research exploration with practical implementation, always keeping the customer experience at the forefront of your work. You will have the autonomy to drive your own research agenda while contributing to team goals and deliverables. About the team The Amazon Customer Service Science team is dedicated to revolutionizing customer support through advanced AI and machine learning. We are a diverse group of scientists and engineers working on some of the most challenging problems in natural language understanding and AI automation. Our team values innovation, collaboration, and a customer-obsessed mindset. We encourage experimentation, celebrate learning from failures, and are committed to maintaining Amazon's high bar for scientific rigor and operational excellence. You will have access to world-class computing resources, massive datasets, and the opportunity to work alongside some of the brightest minds in AI and machine learning.
Applied Scientist, AGI , AGI Information
US, CA, Sunnyvale
Amazon's AGI Information is seeking an exceptional Applied Scientist to drive science advancements in the Amazon Knowledge Graph team (AKG). AKG is re-inventing knowledge graphs for the LLM era, optimizing for LLM grounding. At the same time, AKG is innovating to utilize LLMs in the knowledge graph construction pipelines to overcome obstacles that traditional technologies could not overcome. As a member of the AKG IR team, you will have the opportunity to work on interesting problems with immediate customer impact. The team is addressing challenges in web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory operating over Graphs. You will also have the opportunity to work with scientists working on the other challenges, and with the engineering teams that deliver the science advancements to our customers. A successful candidate has a strong machine learning and agent background, is a master of state-of-the-art techniques, has a strong publication record, has a desire to push the envelope in one or more of the above areas, and has a track record of delivering to customers. The ideal candidate enjoys operating in dynamic environments, is self-motivated to take on new challenges, and enjoys working with customers, stakeholders, and engineering teams to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems. You will collaborate with applied scientists and engineers to develop novel algorithms and modeling techniques to build the knowledge graph that delivers fresh factual knowledge to our customers, and that automates the knowledge graph construction pipelines to scale to many billions of facts. Your first responsibility will be to solve entity resolution to enable conflating facts from multiple sources into a single graph entity for each real world entity. You will develop generic solutions that work fo all classes of data in AKG (e.g., people, places, movies, etc.), that cope with sparse, noisy data, that scale to hundreds of millions of entities, and that can handle streaming data. You will define a roadmap to make progress incrementally and you will insist on scientific rigor, leading by example.
Senior 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 revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As a Senior Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.
Communications Engineer, Simulation and Modeling
US, WA, Redmond
Amazon Leo 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. As a Communications Engineer in Modeling and Simulation, this role is primarily responsible for the developing and analyzing high level system resource allocation techniques for links to ensure optimal system and network performance from the capacity, coverage, power consumption, and availability point of view. Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define novel wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance, such as overall throughput, latency, system availability, packet loss, etc., as well as compatibility for both connectivity and interference mitigation with other space and terrestrial systems. This role in particular will be responsible for 1) evaluating complex multi-disciplinary trades involving RF bandwidth and network resource allocation to customers, 2) understanding and designing around hardware/software capabilities and constraints to support a dynamic network topology, 3) developing heuristic or solver-based algorithms to continuously improve and efficiently use available resources, 4) demonstrating their viability through detailed modeling and simulation, 5) working with operational teams to ensure they are implemented. This role will be part of a team developing the necessary simulation tools, with particular emphasis on coverage, capacity, latency and availability, considering the yearly growth of the satellite constellation and terrestrial network. 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 • Work within a project team and take the responsibility for the Leo's overall communication system design and architecture • Extend existing code/tools and create simulation models representative of the target system, primarily in MATLAB • Design interconnection strategies between fronthaul and backhaul nodes. Analyze link availability, investigate link outages, and optimize algorithms to study and maximize network performance • Use RF and optical link budgets with orbital constellation dynamics to model time-varying system capacity • Conduct trade-off analysis to benefit customer experience and optimization of resources (costs, power, spectrum), including optimization of satellite constellation design and link selection • Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements • Analyze and minimize potential self-interference or interference with other communication systems • Provide visualizations, document results, and communicate them across multi-disciplinary project teams to make key architectural decisions
PhD Econ Intern, Store Economics and Science
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference / structural econometrics skillsets to solve real world problems. The intern will work in the area of Store Economics and Science (SEAS) and develop models to SEAS. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. About the team The Stores Economics and Science Team (SEAS) is a Stores-wide interdisciplinary team at Amazon with a "peak jumping" mission focused on disruptive innovation. The team applies science, economics, and engineering expertise to tackle the business's most critical problems, working to move from local to global optima across Amazon Stores operations. SEAS builds partnerships with organizations throughout Amazon Stores to pursue this mission, exploring frontier science while learning from the experience and perspective of others. Their approach involves testing solutions first at a small scale, then aligning more broadly to build scalable solutions that can be implemented across the organization. The team works backwards from customers using their unique scientific expertise to add value, takes on long-run and high-risk projects that business teams typically wouldn't pursue, helps teams with kickstart problems by building practical prototypes, raises the scientific bar at Amazon, and builds and shares software that makes Amazon more productive.
Principal Applied Scientist
US, WA, Seattle
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 revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced electromechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique 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. Amazon is seeking a talented and motivated Principal Applied Scientist to develop tactile sensors and guide the sensing strategy for our gripper design. The ideal candidate will have extensive experience in sensor development, analysis, testing and integration. This candidate must have the ability to work well both independently and in a multidisciplinary team setting. Key job responsibilities - Author functional requirements, design verification plans and test procedures - Develop design concepts which meet the requirements - Work with engineering team members to implement the concepts in a product design - Support product releases to manufacturing and customer deployments - Work efficiently to support aggressive schedules
Applied Scientist II, Annapurna ML
US, CA, Cupertino
The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You'll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you'll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: - AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization - Machine Learning Compiler: Creating advanced compiler techniques for ML workloads - System Robustness: Building tools for accuracy and reliability validation - Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures A day in the life AWS Utility Computing (UC) provides product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Additionally, this role may involve exposure to and experience with Amazon's growing suite of generative AI services and other cloud computing offerings across the AWS portfolio. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it’s all being enabled by AWS Neuron. Neuron is a Software that include ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Alexa, Amazon Bedrocks, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more.
Applied Scientist, Secure 3P Tools
US, TX, Austin
Amazon Security is seeking an Applied Scientist to work on GenAI acceleration within the Secure Third Party Tools (S3T) organization. The S3T team has bold ambitions to re-imagine security products that serve Amazon's pace of innovation at our global scale. This role will focus on leveraging large language models and agentic AI to transform third-party security risk management, automate complex vendor assessments, streamline controllership processes, and dramatically reduce assessment cycle times. You will drive builder efficiency and deliver bar-raising security engagements across Amazon. Key job responsibilities Own and drive end-to-end technical delivery for scoped science initiatives focused on third-party security risk management, independently defining research agendas, success metrics, and multi-quarter roadmaps with minimal oversight. Understanding approaches to automate third-party security review processes using state-of-the-art large language models, development intelligent systems for vendor assessment document analysis, security questionnaire automation, risk signal extraction, and compliance decision support. Build advanced GenAI and agentic frameworks including multi-agent orchestration, RAG pipelines, and autonomous workflows purpose-built for third-party risk evaluation, security documentation processing, and scalable vendor assessment at enterprise scale. Build ML-powered risk intelligence capabilities that enhance third-party threat detection, vulnerability classification, and continuous monitoring throughout the vendor lifecycle. Coordinate with Software Engineering and Data Engineering to deploy production-grade ML solutions that integrate seamlessly with existing third-party risk management workflows and scale across the organization. About the team Security is central to maintaining customer trust and delivering delightful customer experiences. At Amazon, our Security organization is designed to drive bar-raising security engagements. Our vision is that Builders raise the Amazon security bar when they use our recommended tools and processes, with no overhead to their business. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
Applied Scientist , AWS Healthcare-AI
US, CA, Mountain View
At AWS Healthcare AI, we're revolutionizing healthcare delivery through AI solutions that serve millions globally. As a pioneer in healthcare technology, we're building next-generation services that combine Amazon's world-class AI infrastructure with deep healthcare expertise. Our mission is to accelerate our healthcare businesses by delivering intuitive and differentiated technology solutions that solve enduring business challenges. The AWS Healthcare AI organization includes services such as HealthScribe, Comprehend Medical, HealthLake, and more. We're seeking a Senior Applied Scientist to join our team working on our AI driven clinical solutions that are transforming how clinicians interact with patients and document care. Key job responsibilities To be successful in this mission, we are seeking an Applied Scientist to contribute to the research and development of new, highly influencial AI applications that re-imagine experiences for end-customers (e.g., consumers, patients), frontline workers (e.g., customer service agents, clinicians), and back-office staff (e.g., claims processing, medical coding). As a leading subject matter expert in NLU, deep learning, knowledge representation, foundation models, and reinforcement learning, you will collaborate with a team of scientists to invent novel, generative AI-powered experiences. This role involves defining research directions, developing new ML techniques, conducting rigorous experiments, and ensuring research translates to impactful products. You will be a hands-on technical innovator who is passionate about building scalable scientific solutions. You will set the standard for excellence, invent scalable, scientifically sound solutions across teams, define evaluation methods, and lead complex reviews. This role wields significant influence across AWS, Amazon, and the global research community.
Applied Scientist, Center for Quantum Computing
US, CA, San Francisco
The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in quantum computing for the benefit of our customers. We are looking to hire an Applied Scientist to design and model novel superconducting quantum devices (including qubits), readout and control schemes, and advanced quantum processors. The ideal candidate will have a track record of original scientific contributions, strong engineering principles, and/or software development experience. Resourcefulness, as well as strong organizational and communication skills, is essential. About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either 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, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either 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, or be able to obtain a U.S export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
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