A new paradigm for partnership between industry and academia in the age of AI

How Amazon is shaping a set of initiatives to enable academia-based talent to harmonize their passions, life stations, and career ambitions.

In the global pursuit of top AI talent, the world’s leading scientists and researchers are often faced with a difficult choice between academia and industry. But this choice has potentially debilitating long-term consequences because today’s university professors are the stewards of tomorrow’s talent. When they depart from academia, the pipeline of AI talent available to all sectors — industry, academia, and government — shrinks over time. To avoid this tragedy of the commons, there is a critical need for new paradigms of multisector partnership between academia, government, and industry that enable a better balance of talent across sectors.

Historically, industry has looked to academia for specialized talent across many fields of expertise. And over the years, various models of collaboration have been explored, including academic consulting, private-sector sabbaticals, industry-funded research at universities, and even venture funding that allows university faculty to pursue an entrepreneurial idea for a year or two.

uclaSUREdaysinterior.jpg
In the summer of 2022, the Amazon Summer Undergraduate Research Experience (SURE), held a series of Amazon Days. The Amazon Days were designed to help students gain industry experience as a complement to their research-based summer experience.

But starting in the mid-2010s, with the arrival of deep learning, the need for artificial-intelligence, machine learning, and data science talent grew exponentially and introduced a fundamental change to these longstanding historical mechanisms for industry-academic partnership. Because AI development requires a rare combination of multidisciplinary skillsets, large datasets, and computational capacity, many long-established faculty members with special expertise in AI and machine learning left their academic positions to take full-time roles at tech companies.

This one-way migratory pattern can have lasting and undesirable impact on our top universities. Especially when entire research departments are hired away in bulk, as was the case when a top-ranking US university lost its entire cadre of world-class robotics researchers to a venture-funded enterprise.

At Amazon, we had a need for world-class AI talent, and we challenged ourselves to come up with a solution that would meet that need without causing enduring disruption to the vitality and success of the world’s best universities. We recognized that top AI and machine learning talent is drawn to industry by the sheer scale of the opportunity — to work on big, complex, real-world problems that can change the way we live and work. But in the absence of a more accommodating arrangement, many have to choose to walk away from their passion for nurturing the next generation of great AI talent.

Recognizing the need for new models of partnership that allow industry to tap into academic expertise while strengthening, rather than weakening, our universities, we applied an Amazonian approach by first developing a few tenets:

  • Partnership models should strengthen all participants — industry, academia, and the government.
  • Partnership models should be sustainable and accelerate scientific discovery and the advancement of each sector.
  • Partnership models should provide demonstrable value to all constituents — society at large, faculty, students, university administrations, end customers, government entities, and industrial labs.

These tenets guided the development of the Amazon Scholars program, which allows academics to join Amazon in a flexible capacity, through part-time arrangements and sabbaticals. The program is designed for academics from universities around the globe who want to apply emerging and established research methods in practice, work with real-world systems, and deliver value to end users and society at large. Overcoming some of the hardest technical challenges requires the best expertise in the world to come together, and the Amazon Scholars program allows university faculty to help address these technical challenges at scale while maintaining their professional homes in academia.

To help universities develop the next generation of academic leaders, we expanded the Scholars program in 2020 by adding the Amazon Visiting Academics position, which gives pre-tenure-to-newly-tenured academics an opportunity to stay connected with emergent challenges and opportunities. Amazon is a unique place to measure the impact of new scientific ideas, given the diversity of our customers’ needs and expectations.

The value of the Amazon Scholars program is evidenced by the outstanding quality of academic talent who have joined Amazon as part of this program and its global appeal: we launched the Scholars program in 2019, and today we host more than 250 Scholars and Visiting Academics in 24 cities across the globe, from Los Angeles to Bangalore to Tel Aviv.

In 2019, we launched the University Hubs program to expand the access to academic partnerships beyond the faculty who are Amazon Scholars. The University Hubs program established multiyear commitments of gift and sponsored research funding, PhD fellowships, and community events to promote technology collaborations and community.

Having started with the first Hub at Columbia University, the program now spans five universities. Activity at the Hubs is comanaged by university administrators and Amazon program managers. In 2021, in partnership with Columbia University, we launched the Columbia-Amazon SURE Intern Fellowship program with the goal of providing students from underrepresented backgrounds an opportunity to contribute to cutting-edge research at some of the world’s top universities.

Following the resounding success of the pilot instance of the SURE program at Columbia in the summer of 2021, the SURE program quadrupled the number of supported interns in 2022 and expanded the program to two universities — Georgia Tech and the University of Southern California (USC). This year, the program expanded further, with the additions of Carnegie Mellon University and the University of California, Los Angeles (UCLA).

The impact of SURE
The alumna of the 2021 Columbia SURE Amazon cohort becomes the first Amazon MS Fellow at Columbia.

“The university hub in collaboration with Amazon provides a truly innovative framework for faculty and scholars to continue foundational research in academia or collaborate with industry practitioners in scaling technology breakthroughs to demonstrate real-world impacts,” said Shih-Fu Chang, dean of Columbia Engineering and Morris A. and Alma Schapiro Professor in the Departments of Electrical Engineering and Computer Science. “We are also extremely excited about the success of the SURE program in broadening access to the STEM pipeline by providing the combined resources from both industry and academia to training of students from diverse populations.”

Also in 2019, Amazon launched the Program on Fairness in AI in partnership with the US National Science Foundation (NSF), a $21 million initiative that, since its inception, has provided funding to 34 university-led teams and has produced a growing body of knowledge in the increasingly important area of fair and responsible AI. Amazon also contributes to the NSF’s National AI R&D Institutes program.

The complementary initiatives described above seek to broaden access to emerging technologies by strengthening partnerships across academia, industry, and government. These initiatives enable academia-based talent to harmonize their passions, life stations, and career ambitions by recognizing that academia and industry offer distinct paths to intellectual fulfillment. The success of these initiatives shows that achieving such harmony can enable industrial needs to be met while preserving — and even strengthening — the crucial pipeline of future talent.

Related content

IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
US, CA, Palo Alto
Amazon’s Advertising Technology team builds the technology infrastructure and ad serving systems to manage billions of advertising queries every day. The result is better quality advertising for publishers and more relevant ads for customers. In this organization you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading companies. Amazon Publisher Services (APS) helps publishers of all sizes and on all channels better monetize their content through effective advertising. APS unites publishers with advertisers across devices and media channels. We work with Amazon teams across the globe to solve complex problems for our customers. The end results are Amazon products that let publishers focus on what they do best - publishing. The APS Publisher Products Engineering team is responsible for building cloud-based advertising technology services that help Web, Mobile, Streaming TV broadcasters and Audio publishers grow their business. The engineering team focuses on unlocking our ad tech on the most impactful Desktop, mobile and Connected TV devices in the home, bringing real-time capabilities to this medium for the first time. As a successful Data Scientist in our team, · You are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You will collaborate directly with product managers, BIEs and our data infra team. · You will analyze large amounts of business data, automate and scale the analysis, and develop metrics (e.g., user recognition, ROAS, Share of Wallet) that will enable us to continually measure the impact of our initiatives and refine the product strategy. · Your analytical abilities, business understanding, and technical aptitude will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. · You will have direct exposure to senior leadership as we communicate results and provide scientific guidance to the business. Major responsibilities include: · Utilizing code (Apache, Spark, Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems. · Collaborate with product, BIEs, software developers, and business leaders to define product requirements and provide analytical support · Build customer-facing reporting to provide insights and metrics which track system performance · Influence the product strategy directly through your analytical insights · Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
CA, ON, Toronto
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Sr. Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.
US, CO, Denver
The Fulfillment by Amazon (FBA) enable third-party sellers to use Amazon’s world-class science and logistics infrastructure to supply and fulfill customers worldwide with unprecedented fast delivery promise to customer. In doing so, sellers spend more time building great products, delight customers and grow their business. The FBA team is looking for an Economist intern with strong causal inference and econometrics skills to join our cross-domain group of economists, applied scientists, research scientists, and data scientists. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. 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. Roughly 85% of previous cohorts have converted to full time economist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Bellevue
The Alexa Conversational Assistants Services (CAS) org is looking for a Senior Applied Scientist with a background in Computer Vision, Natural Language Processing, and Large Language Models (LLMs). You will be working with a team of applied and research scientists to enhance existing features and explore new possibilities behind the new Alexa product. Our goal is to make step function improvements in the use of advanced multi-modal LLM models on very large scale computer vision datasets. This is a rare opportunity to develop cutting edge Computer Vision and Deep Learning technologies and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include: * How can multi-modal inputs in LLMs help us deliver delightful conversational experiences to millions of Alexa customers? * Can combining multi-modal data and very large scale LLM models help us provide a step-function improvement to the overall model understanding and reasoning capabilities? We are looking for exceptional scientists who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization. Please visit https://www.amazon.science for more information.