Social

Fall 2021 and winter 2022 Amazon Research Awards recipients announced

Awardees represent more than 30 universities in eight countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.

By Amazon Research Awards team
October 10, 2022
6 min read
Share

The Amazon Research Awards is 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 53 award recipients who represent 38 universities in eight countries.

This announcement includes awards funded under the fall 2021 AWS AI and winter 2022 Alexa: Fairness in AI call for proposals. 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.

Fall 2021 Applied Machine Learning recipients
1 of 4
— Fall 2021 Applied Machine Learning recipients
Top row, left to right: Shirui Pan, Shinji Watanabe, Marco Serafini, Fei Wang, Jing (Jane) Li, Zhihao Jia, Yufei Ding; second row, left to right: Peter Brusilovsky, Callie Hao, Wen-mei Hwu, Tianyi Chen, Bin Yu, Yifan Peng, Sharon Yixuan Li; third row, left to right: Carl Yang, Hui Guan, George Shih, Yonina Eldar, Dimosthenis Karatzas, Vastal Sharan. 
Fall 2021 Machine Learning Algorithms and Theory recipients
2 of 4
— Fall 2021 Machine Learning Algorithms and Theory recipients
First row, left to right: Andrej Risteski, Adel Bibi, Yani Ioannou, Saurabh Prasad, Matteo Sesia; second row, left to right: Hongseok Namkoong, Elias Bareinboim, Pradeep Ravikumar, Ashok Cutkosky, Philip Torr. 
Fall/Winter 2022 Fairness in AI, Computer Vision, Alexa: Fairness in AI recipients
3 of 4
— Fall/Winter 2022 Fairness in AI, Computer Vision, Alexa: Fairness in AI recipients
First row, left to right: Yang Xu, Bolei Zhou, Jana Kosecka, Christopher Potts, Ferdinando Fioretto, Nicolas Papernot; second row, left to right: Snigdha Chaturvedi, Jacob Andreas, Preethi Jyothi, Xiang Ren, David Forsyth, Yuxiong Wang. 
Fall 2021 Natural Language Processing recipients
4 of 4
— Fall 2021 Natural Language Processing recipients
First row, left to right: Tao Yu, Diyi Yang, Shashank Srivastava, Rada Mihalcea, Luis Bentivogli; second row, left to right: Bhuwan Dhingra, Dan Goldwasser, Parisa Kordjamshidi, Yangfeng Ji. 

Recipients have access to more than 300 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.

“Given the ubiquity of machine learning in our daily lives, ensuring that the experiences are fair and equitable has never been more important,” said Rahul Gupta, a senior manager of applied science with Alexa AI. “The breadth of expertise among the 2022 Amazon Research Awards recipients highlights Amazon’s commitment to trustworthy AI research and will bring together experts who are committed to solving intricate, yet important, problems.”

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.

The table below lists, in alphabetical order, fall 2021 AWS AI and winter 2022 Alexa: Fairness in AI cycle call-for-proposal recipients.

Recipient

University

Research title

Jacob Andreas

Massachusetts Institute of Technology

Natural Language Summaries of Deep Networks and Decisions

Elias Bareinboim

Columbia University

Approximate Causal Inference and Decision-Making

Luisa Bentivogli

Fondazione Bruno Kessler

Bias Mitigation and Gender Neutralization Techniques for Automatic Translation

Adel Bibi

University of Oxford

Randomized Smoothing: Future Directions and Extensions

Peter Brusilovsky

University of Pittsburgh

Investigating and Evaluating Exploratory Recommender Systems

Marine Carpuat

University of Maryland, College Park

Model Introspection for Detecting Hallucinations in Neural Machine Translation

Snigdha Chaturvedi

University of North Carolina at Chapel Hill

Task-agnostic Learning of Fair Text Representations and their Application in Natural Language Generation

Tianyi Chen

Rensselaer Polytechnic Institute

Automating Decentralized Machine Learning via Bilevel Optimization

Ashok Cutkosky

Boston University

Private Non-Convex Optimization via Momentum

Bhuwan Dhingra

Duke University

Long-form Question Answering via Collaborative Writing

Yufei Ding

University of California, Santa Barbara

Tensor-centric Acceleration Framework for Large-Scale Deep-Learning Recommendation Model on GPU Clouds

Yonina Eldar

Massachusetts Institute of Technology/Weizmann Institute of Science

Efficient and Interpretable Deep Learning for Low Cost Ultrasound Imaging

Ferdinando Fioretto

Syracuse University

Toward Understanding the Unintended Disparate Impacts of Differentially Private Machine Learning Systems

David Forsyth

University of Illinois Urbana-Champaign

Learning and Evaluating Object Detectors in the All-Novel-Class Regime

Tom Goldstein

University of Maryland

Automated and Efficient Graph Algorithms with AutoGluon and DGL Integration

Dan Goldwasser

Purdue University, West Lafayette

Understanding Socially Grounded Language using Contextualized Discourse Embedding

Hui Guan

University of Massachusetts Amherst

Groot: A GPU-Resident System for Efficient Graph Machine Learning

Callie Hao

Georgia Institute of Technology

Generalizable Zero-shot Auto-tuning for Efficient Deep Learning Workloads Delivery Co-learned with Neural Architecture Search

Wen-mei Hwu

University of Illinois, Urbana–Champaign

Design and Implementation of Storage-Scale Tensors for Efficient GNN Training

Yani Ioannou

University of Calgary

Addressing Catastrophic Forgetting with Dynamic Sparse Training

Yangfeng Ji

University of Virginia

Building Conversational Agents with Limited Resources

Zhihao Jia

Carnegie Mellon University

Towards Affordable and Accessible ML by Leveraging Heterogeneous Spot Instances

Preethi Jyothi

Indian Institute of Technology Bombay

Towards Fairness in Speech Recognition using Targeted Subset Selection and Active Semi-supervised Learning

Dimosthenis Karatzas

Autonomous University of Barcelona

Multipage and Multilingual Document Visual Question Answering

Parisa Kordjamshidi

Michigan State University

Natural Language Instruction Following in Realistic Visual Environments

Jana Kosecka

George Mason University

Hand Shape Modeling for American Sign Language Recognition

Jing (Jane) Li

University of Pennsylvania

HDIBench: An End-to-End Benchmark for High-Dimensional Data Indexing and Searching

Sharon Yixuan Li

University of Wisconsin–Madison

Uncertainty-aware Deep Learning for Reliable Decision Making in an Open World

Rada Mihalcea

University of Michigan

Community-aware Product Question Generation

Hongseok Namkoong

Columbia University

Distributionally Robust Deep Learning Using Pre-trained Models

Shirui Pan

Griffith University

Effective Multi-Task Self-Supervised Learning for Graph Anomaly Detection

Nicolas Papernot

University of Toronto

Characterizing the Privacy Attack Surface of Machine Learning

Yifan Peng

Weill Cornell Medicine

Modeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports

Christopher Potts

Stanford University

Causal abstractions of neural networks: Towards more explainable models and generalization guarantees

Saurabh Prasad

University Of Houston

Steerable Sparse Deep Neural Networks and Knowledge Transfer for Robust GeoAI

Pradeep Ravikumar

Carnegie Mellon University

Causal + Deep Out-of-Distribution Learning

Xiang Ren

University of Southern California

Generating and Utilizing Explanations for Human-in-the-Loop Language Model Refinement

Andrej Risteski

Carnegie Mellon University

Causal + Deep Out-of-Distribution Learning

Marco Serafini

University of Massachusetts Amherst

Groot: A GPU-Resident System for Efficient Graph Machine Learning

Matteo Sesia

University of Southern California

CONFORMALIZED LEARNING FOR UNCERTAINTY-AWARE AI

Vatsal Sharan

University of Southern California

Actionable Insights at Scale: Certified Anomaly Detection for Data-Intensive Systems

George Shih

Weill Cornell Medicine

Modeling longitudinal EHR to compose interpretable, deep knowledge-enhanced radiology reports

Shashank Srivastava

University of North Carolina At Chapel Hill

Learning from Natural Language Explanations for the Long Tail

Philip Torr

University of Oxford

Randomized Smoothing: Future Directions and Extensions

Yuxiong Wang

University of Illinois At Urbana–Champaign

Learning and Evaluating Object Detectors in the All-Novel-Class Regime

Fei Wang

Cornell University

High-Throughput Drug Repurposing with Real World Data Enhanced with Biomedical Knowledge

Shinji Watanabe

Carnegie Mellon University

Non-Autoregressive Conversational Speech Recognition

Yang Xu

University of Toronto

Developing machine comprehension and fairness toward informal language

Carl Yang

Emory University

Federated Learning on Graph Data: Utility, Efficiency, and Privacy

Diyi Yang

Georgia Institute of Technology

Learning Continually and Adaptatively for Natural Language Processing

Tao Yu

University of Hong Kong

Scalable Conversational Structured Knowledge Grounding with a Unified Language Model

Bin Yu

UC Berkeley

Interpretable and Stable AutoML

Bolei Zhou

University of California, Los Angeles

Improving Out-of-Distribution Generalization through Steerable Generative Modeling.
Tags
About the Author
Amazon Research Awards team

Related content

Work with us

See more jobs See more jobs
Applied 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 (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on 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 * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
Applied Scientist, Fauna
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
Applied Scientist II, Translation Services
IN, TS, Hyderabad
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Hyderabad office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
Applied Scientist, Navigation
US, CA, Sunnyvale
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
Applied Scientist II, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code
Applied Scientist, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities 1. Research the problem domain and come up with various approaches to solve the problem. 2. Be willing to experiment quickly and fail fast. 3. Collaborate with engineers to come up with the right end to end solution to the business problems. 4. Ideate on future roadmap for science in CMT 5. Be willing to roll up your sleeves and learn core topics outside applied science, for example ML engineering A day in the life A typical day might involve (a) working on ideas for improving models around product similarity or price recommendations, (b) working closely with other scientists and our ML engineers to ensure that the best models are in production, (c) writing good maintainable code that can be reused and reproduced, (d) sharing your work across CMT and beyond via technical writings and presentations
Applied Scientist II, Reinforcement Learning
US, MA, N.reading
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 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 an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous 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. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. 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. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for. In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo). Key job responsibilities - Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks. - Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution. - End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks. - Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement. - Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries. - Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards. Basic Qualifications - PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. - 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production. - Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design. - Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization. - Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment. Preferred Qualifications - Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure). - Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications. - Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services. - Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML). - Experience in designing, executing, and evaluating rigorous online A/B experiments.
Senior Applied Scientist, Amazon Music - Search Science, Amazon Music – Search Science
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is looking for an execution-focused Senior Applied Scientist to spearhead core scientific initiatives within our Bangalore hub. In this leadership-by-example role, you will define and execute the applied science roadmap for search and content discovery systems, directly impacting millions of customers worldwide. You will operate at the exciting intersection of large-scale search infrastructure, applied machine learning, and foundation models. You will drive immediate, high-impact business deliverables in music search relevance, personalization, and retrieval performance, while simultaneously architecting the long-term technological vision that expands our search ecosystem toward deeper semantic intelligence, agentic workflows, and cross-domain audio understanding. Key job responsibilities - Strategic Roadmap & Architecture: Define and execute the technical and scientific roadmap for music search systems, making critical architectural decisions that balance short-term feature delivery with long-term scalability, maintainability, and extensibility. - Pioneering Applied Science: Lead the design and implementation of state-of-the-art machine learning solutions leveraging deep learning, LLMs, and agentic workflows to solve complex search, ranking, and intent-matching challenges. - Cross-Functional Leadership: Partner closely with product management, engineering leaders, and peer teams to harmonize technical direction and deliver synchronized customer experiences. - Technical Excellence & Mentorship: Drive engineering and scientific excellence across the team by conducting rigorous design reviews, establishing modeling best practices, setting high bars for artifact delivery, and mentoring junior/mid-level scientists. - Experimentation & Scaling: Establish robust scientific processes for large-scale data analysis, offline model validation, and online A/B experimentation, ensuring high statistical rigor and measurable business impact across millions of active listeners. - Stakeholder Influence & Writing: Author strategic whitepapers, and executive-level documentation. Communicate complex technical options and trade-offs to senior leadership to drive informed decision-making. Basic Qualifications - PhD, or Master’s degree and 6+ years of applied research and industrial machine learning experience in Computer Science, Machine Learning, or a related field. - 3+ years of specialized experience designing, building, and scaling machine learning models for core production business applications (e.g., Search, Recommendation Systems, or Large-Scale Information Retrieval). - Expert programming proficiency in Python, Java, C++, or related languages, combined with deep familiarity with neural network architectures and deep learning frameworks. - Proven track record of owning end-to-end technical deliverables from problem formulation and model architecture to production deployment and performance tuning. - Demonstrated leadership in mentoring technical talent and driving engineering/scientific best practices. Preferred Qualifications - Deep expertise in search retrieval, query understanding, ranking algorithms, and large-scale vector search/embedding systems. - Experience building applied science solutions on top of foundation models, large language models (LLMs), or multi-modal architectures. - Experience with large-scale distributed training and inference optimization on cloud infrastructure (AWS). - A strong publication record or patent portfolio in top-tier peer-reviewed venues (e.g., NeurIPS, SIGIR, KDD, ACL, ICML). - Experience designing and interpreting complex online experimentation frameworks for consumer-facing recommendation or search products.
Principal Applied Scientist, CX Science
US, CA, Santa Clara
We are looking for passionate, talented, and inventive Principal Applied Scientist with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our team, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology: NLU, Dialog Management, Conversational AI, LLMs and Generative AI. A day in the life The team uses generative AI and foundation models to reimagine the experience of all customers on AWS. We explore new technologies and find creative solutions. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space and want to enlighten your peers with new capabilities, this is the team for you.
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