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December 6 - 14, 2021
Virtual
Conference
NeurIPS 2021

Overview

The Neural Information Processing Systems (NeurIPS) annual meeting fosters the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their fields.

Sponsorship Details

Conference website
Related research
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Amazon program committee and board members

Accepted publications (partial list)

Workshops

NeurIPS 2021 Workshop on Queer in AI
Unknown date
Amazon organizer: Luca Soldaini

Website: https://sites.google.com/view/queer-in-ai/neurips-2021?authuser=0
NeurIPS 2021 Workshop on Learning in Presence of Strategic Behavior
December 13, 12:00 PM - 5:50 PM EST
Amazon speaker: Michael I. Jordan (Amazon Scholar)

Website: https://sites.google.com/view/strategicml/about
NeurIPS 2021 Workshop on Women in Machine Learning (WiML 2021)
December 10
Amazon organizer: Mariya Vasileva

Website: https://sites.google.com/view/wiml2021/home
NeurIPS 2021 Workshop on Efficient Natural Language and Speech Processing
December 13, 8:00 AM - 8:00 PM EST
Amazon organizers: Di Jin, Peyman Passban, Tanya Roosta, Ankit Chadha, Aashiq Muhamed, Iman Keivanloo

Website: https://neurips2021-nlp.github.io/
Communication-efficient federated learning for neural machine translation
Tanya Roosta, Peyman Passban, Ankit Chadha
2021
Conversational AI / Natural-language processing
Magic pyramid: Accelerating inference with early exiting and token pruning
Xuanli He, Iman Keivanloo, Yi Xu, XIANG He, Belinda Zeng, Santosh Rajagopalan, Trishul Chilimbi
2021
Machine learning
Towards continual entity learning in language models for conversational agents
Ravi Teja Gadde, Ivan Bulyko
2021
Conversational AI / Natural-language processing
Towards zero- and few-shot knowledge-seeking turn detection in task-oriented dialogue systems
Di Jin, Shuyang Gao, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tür
2021
Related: Alexa AI team discusses NeurIPS workshop best paper award
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Causal Inference & Machine Learning: Why now?
December 13, 9:30 AM - 6:00 PM EST
Amazon speaker: Victor Chernozhukov (Amazon Scholar)

Amazon organizer: Bernhard Schölkopf

Website: https://why21.causalai.net/
NeurIPS 2021 Workshop on Metacognition in the Age of AI: Challenges and Opportunities
December 13, 8:00 AM - 5:30 PM EST
Amazon speaker: Bernhard Schölkopf

Website: https://sites.google.com/view/metacogneurips2021/home
NeurIPS 2021 Workshop on Machine Learning meets Econometrics (MLECON)
December 13, 8:00 AM - 3:10 PM EST
Amazon speaker: Eric Tchetgen Tchetgen (Amazon Scholar)

Website: https://sites.google.com/view/mlecon2021/home
NeurIPS 2021 Workshop on Databases and AI (DBAI)
December 13, 8:50 AM - 5:45 PM EST
Website: https://dbai-workshop.github.io/
DP-KB: Data programming with knowledge bases improves transformer fine tuning for answer sentence selection
Nic Jedema, Thuy Vu, Manish Gupta, Alessandro Moschitti
2021
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Safe and Robust Control of Uncertain Systems
December 13, 11:00 AM - 7:00 PM EST
Website: https://sites.google.com/view/safe-robust-control/home
Robust reinforcement learning for shifting dynamics during deployment
Samuel Stanton, Rasool Fakoor, Jonas Mueller, Andrew Gordon Wilson, Alex Smola
2021
Machine learning
NeurIPS 2021 Workshop on New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership
December 13, 8:30 AM - 7:00 PM EST
Amazon organizer: Nghia Hoang, Anoop Deoras

Website: https://neurips2021workshopfl.github.io/NFFL-2021/index.html
NeurIPS 2021 Workshop on Human Centered AI (HCAI)
December 13
Website: https://sites.google.com/view/hcai-human-centered-ai-neurips/home
Interpreting voice assistant interaction quality from unprompted user feedback
Pragati Verma, Sudeeksha Murari
2021
Conversational AI / Natural-language processing
NeurIPS 2021 Workshop on Causal Sequential Decisions
December 14
Amazon speaker: Rui Song (Amazon Scholar)

Website: https://sites.google.com/view/causal-sequential-decisions/home
NeurIPS 2021 Workshop on Out-of-Distribution Generalization and Adaptation in Natural and Artificial Intelligence
December 14, 8:00 AM - 3:20 PM EST
Amazon speaker: Bernhard Schölkopf

Website: https://docs.neurodata.io/ood-workshop/committee
NeurIPS 2021 Workshop on Privacy in Machine Learning
December 14, 11:10 AM - 8:55 PM EST
Amazon speaker: Aaron Roth (Amazon Scholar)

Website: https://priml2021.github.io/
Reconstructing test labels from noisy loss scores
Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2021
Security, privacy, and abuse prevention
NeurIPS 2021 Workshop on Learning and Decision-Making with Strategic Feedback (StratML'21)
December 14, 10:00 AM - 5:15 PM EST
Amazon organizer: Aaron Roth (Amazon Scholar)

Website:  https://www.strategic-ml.com/
NeurIPS 2021 Workshop on Data-Centric AI
December 14
Website: https://datacentricai.org/
Comparing data augmentation and annotation standardization to improve end-to-end spoken language understanding models
Leah Nicolich-Henkin, Taichi Nakatani, Zach Trozenski, Joel Whiteman, Nathan Susanj
2021
Conversational AI / Natural-language processing
A first look towards one-shot object detection with SPOT for data-efficient learning
Ria Chakraborty, Madhur Popli, Rachit Lamba, Rishi Verma
2021
Computer vision
NeurIPS 2021 Workshop on Distribution Shifts
December 13
Website: https://sites.google.com/view/distshift2021
Just mix once: Mixing samples with implicit group distribution
Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao
2021
Computer vision
Gradient-matching coresets for continual learning
Lukas Balles, Giovanni Zappella, Cédric Archambeau
2021
Machine learning
NeurIPS 2021 Workshop on I (Still) Can't Believe It's Not Better
December 13
Website: https://i-cant-believe-its-not-better.github.io/neurips2021/
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
Ke Alexander Wang, Danielle Robinson, Yuyang (Bernie) Wang
2021
Machine learning
Language models as recommender systems: Evaluations and limitations
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, Hao Wang
2021
Machine learning

Virtual Events

A conversation with AI luminaries
December 2, 1:00 PM - 2:00 PM EST
Michael I. Jordan, Michael Kearns, and Bernhard Schölkopf discuss the future of AI
Panelists
Michael Jordan
Amazon Scholar
Michael Kearns
Amazon Scholar
Bernhard
Bernhard Schölkopf
VP, Distinguished Scientist
Database & AI Leadership
Watch the replay
The challenge and promise of quantum computing
December 15, 1:00 PM - 2:15 PM EST
Join seven Amazon scientists, Scholars and Visiting Academics as they discuss the current and future challenges of quantum computing.
Panelists
Simone Severini
Director, Applied Science
Antia Lamas-Linares
Senior Manager, Research Science
Earl Campbell
Senior Research Scientist
John Preskill
Amazon Scholar
Katharine Hyatt
Applied Scientist
James Whitfield
Amazon Visiting Academic
Helmut Katzgraber
Senior Manager, Research Science
Register here
Deep dive with Prime Video leaders
December 13, 12:30 PM - 1:30 PM EST
In this session, the panelists will review how they utilize multimodal machine learning and computer vision to delight millions of Amazon Prime Video customers in 200+ countries. Join as they explore the challenges of solving video understanding at scale, and highlight recent efforts in improving trust, safety, integrity, personalization, quality, and visual effects for video on demand and live content.
Panelists
Ahmed Saad
Senior Manager, Applied Science
Vimal Bhat
Senior Manager, Applied Science
Caren Chen
Manager, Applied Science
Andrew Collins
Senior Manager, Software Development
Register here
NeurIPS 2021: Connecting women in science
December 7, 11:00 PM EST
Join our virtual event and hear from women in tech about their experience at Amazon and why they are passionate about their teams and science. Open only to registered NeurIPS attendees.
Panelists
Cecile Levasseur
Senior Manager, Applied Science
Tara Taghavi
Manager, Applied Science
Rashmi Gangadharaiah
Principal, Applied Scientist
Learn more
Amazon Scholars Michael I. Jordan and Michael Kearns and Amazon distinguished scientist Bernhard Scholkopf NeurIPS Amazon Science.jpg
NeurIPS luminaries on the future of AI
Larry Hardesty
December 02, 2021
Amazon Science hosts a conversation with Amazon Scholars Michael I. Jordan and Michael Kearns and Amazon distinguished scientist Bernhard Schölkopf.
Machine learning

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To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Do you have a strong machine learning background and want to help build new speech and language technology? Amazon is looking for PhD students who are ready to tackle some of the most interesting research problems on the leading edge of natural language processing. We are hiring in all areas of spoken language understanding: NLP, NLU, ASR, text-to-speech (TTS), and more! A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will develop and implement novel scalable algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, NLP, search, and deep learning. You will work side-by-side with global experts in speech and language to solve challenging groundbreaking research problems on production scale data. The ideal candidate must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon has positions available for Natural Language Processing & Speech Intern positions in multiple locations across the United States. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. Please visit our website to stay updated with the research our teams are working on: https://www.amazon.science/research-areas/conversational-ai-natural-language-processing
2023 Fall Research Science Intern - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
2023 Fall Research Science Internship - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
2023 Fall Applied Science Internship - Canada
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
2023 Fall Applied Science Internship - Canada
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
2023 Fall Applied Science Internship - Automated Reasoning and Storage Systems - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for Masters or PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
2023 Fall Applied Science Internship - Automated Reasoning and Storage Systems - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
2023 Fall Applied Science Internship - Computer Vision and Robotics - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science
2023 Fall Applied Science Internship - Computer Vision and Robotics - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science
2023 Fall Applied Science Internship - Machine Learning - United States
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters or PhD student interested in machine learning? We are looking for skilled scientists capable of putting Machine Learning theory into practice through experimentation and invention, leveraging machine learning techniques (such as random forest, Bayesian networks, ensemble learning, clustering, etc.), and implementing learning systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
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