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, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, the Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.
JP, Tokyo
The Amazon Logistics (AMZL) Team is responsible for the acquisition, design, construction, and management of all facilities in the Amazon Delivery Station Network. AMZL is looking for a talented and passionate Data Scientist to help shape its Last Mile business with technical strategies and solutions, by processing, analyzing and interpreting huge data sets. You should be comfortable with ambiguity, problem solving and enjoy working in a fast-paced, diverse and dynamic environment. Using analytical rigor and statistical methods, you mine through data to identify opportunities for Amazon and our delivery channels. And you collaborate with other scientists, engineers, Product and Program Managers to deploy new products and solutions. [More Information] Last Mile Department Data Analyst/BI Engineer Tokyo Office *Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, visit https://www.amazon.jobs/disability/jp Key job responsibilities Creating a roadmap of the most challenging business questions and use data to articulate possible root cause analysis and solutions Managing and executing entire projects or components of large projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights Partnering with Product, Program and Engineering teams to design and run models, research new algorithms, and prove incrementality and drive growth Understanding drivers, impacts, and key influences on seller growth dynamics Developing and scaling end-to-end ML Models and solutions Automating feedback loops for algorithms in production Utilizing Amazon systems and tools to effectively work with terabytes of data About the team Last Mile Execution Analytics (LMEA) team of JP works as an integral part of Amazon Logistics to ensure that its business intelligence, analytics, tools and planning needs are met. By providing information, insight, and decision support, we strive to enable success of all parts of AMZL. Our customer set includes senior management, station operations, external vendors, long-term planning, Ops technology (Voice of the Delivery Station, Voice of the Customer), network planning, and pretty much every BI and Ops teams. Voice of Employee [Work Life Harmony] We believe, it is important to spend private time such as spending time with your family or doing anything you like to spur innovation. Amazon promotes a fulfilling and flexible work style according to the work volume and lifestyle of each employee.
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
LU, Luxembourg
Are you a talented and inventive scientist with a strong passion about modern data technologies and interested to improve business processes, extracting value from the data? Would you like to be a part of an organization that is aiming to use self-learning technology to process data in order to support the management of the procurement function? The Global Procurement Technology, as a part of Global Procurement Operations, is seeking a skilled Data Scientist to help build its future data intelligence in business ecosystem, working with large distributed systems of data and providing Machine Learning (ML) and Predictive Modeling expertise. You will be a member of the Data Engineering and ML Team, joining a fast-growing global organization, with a great vision to transform the Procurement field, and become the role model in the market. This team plays a strategic role supporting the core Procurement business domains as well as it is the cornerstone of any transformation and innovation initiative. Our mission is to provide a high-quality data environment to facilitate process optimization and business digitalization, on a global scale. We are supporting business initiatives, including but not limited to, strategic supplier sourcing (e.g. contracting, negotiation, spend analysis, market research, etc.), order management, supplier performance, etc. We are seeking an individual who can thrive in a fast-paced work environment, be collaborative and share knowledge and experience with his colleagues. You are expected to deliver results, but at the same time have fun with your teammates and enjoy working in the company. In Amazon, you will find all the resources required to learn new skills, grow your career, and become a better professional. You will connect with world leaders in your field and you will be tackling Data Science challenges to ensure business continuity, by taking the right decisions for your customers. As a Data Scientist in the team, you will: -be the subject matter expert to support team strategies that will take Global Procurement Operations towards world-class predictive maintenance practices and processes, driving more effective procurement functions, e.g. supplier segmentation, negotiations, shipping supplies volume forecast, spend management, etc. -have strong analytical skills and excel in the design, creation, management, and enterprise use of large data sets, combining raw data from different sources -provide technical expertise to support the development of ML models to facilitate intelligent digital services, such as Contract Lifecycle Management (CLM) and Negotiations platform -cooperate closely with different groups of stakeholders, e.g. data/software engineers, product/program managers, analysts, senior leadership, etc. to evaluate business needs and objectives to set up the best data management environment -create and share with audiences of varying levels technical papers and presentations -deal with ambiguity, prioritizing needs, and delivering results in a dynamic environment Basic qualifications -Master’s Degree in Computer Science/Engineering, Informatics, Mathematics, or a related technical discipline -3+ years of industry experience in data engineering/science, business intelligence or related field -3+ years experience in algorithm design, engineering and implementation for very-large scale applications to solve real problems -Very good knowledge of data modeling and evaluation -Very good understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc. -SQL and query performance tuning skills Preferred qualifications -2+ years of proficiency in using R, Python, Scala, Java or any modern language for data processing and statistical analysis -Experience with various RDBMS, such as PostgreSQL, MS SQL Server, MySQL, etc. -Experience architecting Big Data and ML solutions with AWS products (Redshift, DynamoDB, Lambda, S3, EMR, SageMaker, Lex, Kendra, Forecast etc.) -Experience articulating business questions and using quantitative techniques to arrive at a solution using available data -Experience with agile/scrum methodologies and its benefits of managing projects efficiently and delivering results iteratively -Excellent written and verbal communication skills including data visualization, especially in regards to quantitative topics discussed with non-technical colleagues
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, 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. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust Balance Work and Life 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 Mentor & Grow Careers 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 • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables
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