Prime Video call for proposals — Fall 2022

Pushing the boundaries of science and technology

About this CFP:

With the mission to be the world's most loved entertainment service, Prime Video continually strives to delight its customers by offering them the most engaging video-watching experiences. Prime Video works to solve a broad range of cutting-edge technical problems. We welcome proposals related to the following broad research areas in order to accelerate progress in the state of the art of video-watching.

Research Area 1 : Anomaly Detection and Insights

Undisrupted entertainment via large-scale anomaly detection

Overview: Customers should be able to reliably stream content at all times using any device where the application is available. This scope results in a combinatorial explosion of metrics that individually describe the quality of service across different marketplaces, regions, and devices. Monitoring such metrics in real-time is crucial for ensuring that any faults are immediately identified and resolved with minimal user disruption, and we are investing heavily in building robust and scalable anomaly detection tools to meet this requirement.

Research sub-areas: We are looking to fund research in the following sub-areas:

a. Anomaly detection on intermittent time series: Balancing the trade-off between maintaining high precision while also minimizing time-to-detection is a key challenge for time series anomaly detection. This is especially pertinent to the case of intermittent time series, whereby seasonality patterns in the time series may only become observable when data is aggregated over coarser periods of time.

Examples of research questions include:

  • How can representations of time series at different levels of granularity be leveraged for detecting anomalies?
  • For metrics with different levels of sparsity, how can we ensure that optimal time-to-detection is achieved for both smooth and intermittent metrics?
  • How can we ensure that derived anomaly scores remain consistent and interpretable across all time series?

b. Anomaly detection for multivariate time series: Collections of monitorable time series are often closely correlated or even generated from the same underlying process, and would benefit from being jointly modeled. However, this may also introduce additional complexity when individual time series have dimensions that are either missing at random or altogether. Adapting to such data challenges is crucial to enabling robust anomaly detection across groupings of related time series.

Examples of research questions include:

  • Are generative models effective for multivariate time series anomaly detection?
  • How can we apply multivariate forecasting-based anomaly detection when individual multi-dimensional time series have missing or additional dimensions?
  • How can we convert global anomaly scores emitted from a multivariate anomaly detection model to individual scores across specific dimensions?

c. Anomaly detection in time series with cold start
Monitoring capabilities to be enabled without extended warm-up period are important. The robustness of newly-deployed anomaly detection models often relies on having sufficient examples of normal and anomalous data before launch, but how do we handle cases where only few anomaly samples are available (if any at all)? The notion of transfer learning or meta-learning is commonplace in other domains, but has received little attention for time series anomaly detection.

Examples of research questions include:

  • How can we jointly monitor new time series with ramp-up behavior (e.g. sessions on newly-launched devices) alongside related metrics having longer and smoother historical data?
  • How can we determine the decision boundary for anomaly detection in a time series with no previous anomalies?
  • As more labels become available over time, how can we efficiently augment already-existing models without having to retrain them from scratch?

Proposal Requirements: Proposals should be prepared according to the proposal template. In addition, to submit a proposal for this CFP, please also include the following information:

  1. Describe current applications of your work (e.g, libraries, codebases and industry code).
  2. What are potential applications of your work?
  3. What assumptions are made by your work (e.g., that affect soundness, precision, and/or scalability)?
  4. If your work involves the development and maintenance of a tool:
    1. What license is your tool released under?
    2. What on-boarding/tutorial material is available?
    3. Is your tool actively maintained (commits within last 3 months)? How many active contributors does your project have?
  5. What data are you planning to work with?

Research Area 2 : Personalization and Discovery

Personalized recommendations and discovery

Overview: The mission of this call for proposal is to improve engagement by providing relevant, personalized and timely recommendations. We guide users to discover content, and to stream stories relevant to their interests -current and emerging- while providing users a personalized experience that is transparent and builds trust in our brand.

Research sub-areas: We are looking to fund research in the following sub-areas:

a. Representation learning for title/user understanding: This topic includes but is not limited to the following research areas: Developing new representation techniques for different entities in RS; Understanding and evaluating existing representations, e.g. probing representations for generalization, compositionality & robustness, adversarial evaluation, analysis of representations; Efficient learning of representations and inference with respect to training and inference time, model size, amount of training data, etc.

b. Reinforcement learning for title recommendation/page composition: Reinforcement Learning (RL) is a sequential decision making technique which maximizes the notion of long-term rewards. Framing title/carousel recommendations as building RL agents that maximize user satisfaction will enable us to explore and extend recent RL developments. Under this call for proposals, we are specifically seeking proposals on 1. RL Applications in recommender systems 2. Page composition 2. Real-world challenges and best practices for RL e.g. effective real-world exploration strategies, the role of offline and online metrics for diagnostics and modeling, Real-time inference and scalable ML workflows, hyper-parameter tuning for RL, interpretability, scalability and exploratory data analysis. 3. RL algorithms and evaluation, e.g. data driven, offline, and batch reinforcement learning; off-policy learning and counterfactual evaluation; deep RL and multi-arm Bandits; bandits for non-stationary environments.

c. New deep learning architecture for recommendation: Deep learning based recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user. The topic will focus on new learning paradigms & architecture for recommendation systems.

d. Trustworthy AI for recommendation: Recommendation systems may lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization et al. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. The topic will focus on fairness and bias, explainability, diversity, causal inference, accountability, and privacy across recommendation systems.

Proposal Requirements: Proposals should be prepared according to the proposal template.

Research Area 3 : Video Quality Analysis

Eliminating media quality defects

Overview: The ability to accurately detect and characterize media quality defects across both new and existing formats is critical.

Research sub-areas: We are looking to fund research in the following sub-areas:

a. Extended parameter space: While new media formats such as UHD, HDR and Dolby Atmos deliver unparalleled immersive experiences, there is a limited understanding of their interaction within the wider extended parameter space (e.g. resolution, bitrate, content etc) and the impact on media quality. This will be a key driver in the development of novel technologies to identify audio and/or visual quality defects across this space.

b. Context: Signal processing techniques are typically applied on audio, video and/or closed captions to detect media quality defects. Context (e.g. genre, creative intent, metadata), and interactions between these signal modalities, are often ignored. Methodologies which can exploit and disentangle this additional information will likely be more precise, robust and explainable.

c. Defect synthesis: There exists a long tail of media quality defects, and as a consequence obtaining ground truth examples can be prohibitive and expensive. Synthesizing these defects poses a number of challenges including defect modeling, data imbalance and a potential domain gap between synthetic and real data. Additionally it can be difficult to both characterize and have confidence in detecting low-prevalence and/or unseen defects. Addressing these problems is crucial in ensuring that consistent experience is delivered to all users.

Proposal Requirements: Proposals should be prepared according to the proposal template, and can be video, audio and/or audiovisual based.

Timeline

Submission period: September 16 to October 26, 2022

Decision letters will be sent out March 2023

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $50,000 USD on average
  • AWS Promotional Credits, no more than $40,000 USD on average
  • Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page.

Proposal requirements

Proposals should be prepared according to the proposal template. In addition, to submit a proposal for this CFP, please also include the following information:

  1. Please list the open-source tools you plan to contribute to.
  2. Please list the AWS ML tools you will use.

Selection criteria

ARA will make the funding decisions based on the potential impact to the research community and quality of the scientific content.

Expectations from recipients

To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

US, WA, Seattle
Job description: We are reimagining Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, (Bayesian) time series, macroeconomic, as well as basic familiarity with Matlab, R, or 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 at Internet speed 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 economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
RO, Iasi
Amazon’s mission is to be earth’s most customer-centric company and our team is the guardian of our customer’s privacy. Amazon SDO Privacy engineering operates in Austin – TX, US and Iasi, Bucharest – Romania. Our mission is to develop services which will enable every Amazon service operating with personal data to satisfy the privacy rights of Amazon customers. We are working backwards from the customers and world-wide privacy regulations, think long term, and propose solutions which will assure Amazon Privacy compliance. Our external customers are world-wide customers of Amazon Retail Website, Amazon B2B services (e.g. Seller central, App / Skill Developers), and Amazon Subsidiaries. Our internal customers are services within Amazon who operate with personal data, Legal Representatives, and Customer Service Agents. You can opt-in for being part of one of the existing or newly formed engineering teams who will contribute to Amazon mission to meet external customers’ privacy rights: Personal Data Classification, The Right to be forgotten, The right of access, or Digital Markets Act – The Right of Portability. The ideal candidate has a great passion for data and an insatiable desire to learn and innovate. A commitment to team work, hustle and strong communication skills (to both business and technical partners) are absolute requirements. Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems. Your solutions will apply to all of Amazon’s consumer and digital businesses including but not limited to Amazon.com, Alexa, Kindle, Amazon Go, Prime Video and more. Key job responsibilities As an data scientist on our team, you will apply the appropriate technologies and best practices to autonomously solve difficult problems. You'll contribute to the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. You will collaborate with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. Your work will directly impact the trust customers place in Amazon Privacy, globally.
JP, 13, Tokyo
The JP Economics team is a central science team working across a variety of topics in the JP Retail business and beyond. We work closely with JP business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP- EU- and US-based interdisciplinary teams. In this role, you will build ground-breaking, state-of-the-art causal inference models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, econometrics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. Contribute to building a strong data science community in Amazon Asia.
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.
US, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.
Amazon Research Awards.jpg

Amazon Research Awards

Collaborating with scientists around the world to fund research, share knowledge and encourage innovation.