Social

SocialBot Grand Challenge FAQs

Frequently asked questions about the challenge.
General
General
What is a SocialBot?
In the context of Alexa Prize, a SocialBot is an Alexa skill that can engage customers in a conversation about popular topics such as entertainment, sports, politics, technology, and fashion.
Can I choose to build any kind of conversational bot?
No, this competition focuses solely on SocialBots. Your SocialBot will need to converse about a wide range of popular societal topics and current events, and not only topics of your choosing. You will develop your own technologies relevant to your approach, such as natural language understanding, dialog management, knowledge acquisition, commonsense reasoning, conversational planning, machine learning, natural language generation, etc. Far-field automatic speech recognition (ASR) and text-to-speech (TTS) will be provided by Amazon, along with additional data, models, and tools.
What will my SocialBot do?
Your SocialBot will be an Alexa skill that can converse coherently and engagingly with humans on popular topics and current events. If your SocialBot advances to the pertinent stage of the competition, Alexa users in the U.S. may ask to converse with your SocialBot about topics of interest to them, such as baseball playoffs, celebrity gossip, or scientific breakthroughs. For example:
  • User: Let’s chat about the Mars Mission.
  • SocialBot: There are multiple Mars missions, some public and some private.
  • User: Who do you think will succeed?
  • SocialBot: I think more than one will succeed. The first one to get to Mars will open the doors for others.
  • User: I’d love to go to Mars.
  • SocialBot: Me too. Luckily I can hitch a ride on any computer.
  • User: That’s very funny.
  • SocialBot: The biggest challenge isn’t technology, it’s funding. It will cost $500 billion to send humans to Mars.
Your SocialBot will continue turn-by-turn interaction, starting with a topic the user asked for, until the user chooses to stop. Like an everyday human conversation, the interaction may shift naturally to related topics, as in the above example, but the conversation should remain coherent, relevant, and engaging. Your SocialBot may suggest topics to keep the conversation flowing. The goal is to keep the conversation from deteriorating to the point where the user loses interest.
How will I build my SocialBot?
You will use the Alexa Skills Kit (ASK) to build an Alexa skill, hosted on AWS Lambda, that will create the end-to-end conversational experience for a user. Using the provided APIs, your skill will receive as input the text of the user’s utterance, and produce as output a text sentence that will be spoken to the user. You do not need to tackle ASR (automatic speech recognition) or TTS (text to speech). You will also be provided with the CoBot Toolkit (a conversational bot toolkit), a software development kit that works with ASK and was built specifically for Alexa Prize teams to reduce the involved engineering in setting up a SocialBot and allow teams to focus on the science.


Your skill will need to determine an appropriate response at each turn of the conversation. It will also need to keep up with current news and events using the provided data sources. You may use additional data sources or libraries if you wish, subject to the terms described in the Official Rules.
What is the Alexa Skills Kit (ASK)?
The Alexa Skills Kit (ASK) is a collection of free, self-service APIs, tools, documentation, and code samples that make it fast and easy for you to add skills to Alexa. Your team will use ASK to build, deploy, and test a SocialBot that is capable of conversing with millions of Alexa users.
Competition details
What is the goal of the challenge?
The goal of the SocialBot Grand challenge is to advance several areas of conversational AI including natural language understanding (NLU), context modeling, dialog management, commonsense reasoning, natural language generation (NLG), and knowledge acquisition. The grand challenge objective is to create a SocialBot that converses coherently and engagingly with humans on popular topics for 20 minutes while achieving a user rating of at least 4.0/5.0.
How will winners be selected?
Through various phases of the competition, SocialBots will be evaluated based on feedback from Alexa users and assessment by Amazon.


Following the initial feedback period, SocialBots that have been certified and published will be evaluated on criteria such as the average interaction rating, uptime requirements, and ability to filter offensive content in order to advance to the Semifinals Interaction Period.



During the semifinals interaction period, Alexa customers will evaluate the semifinalist SocialBots. The two SocialBots with the highest Semifinals Interaction Rating Average and up to three more SocialBots selected by Amazon will advance to the finals.



Teams that advance to and complete the Semifinals Interaction Period, regardless of whether they advanced to the Finals Event, will be eligible to compete for Scientific Invention and Innovation Prizes based on the level of scientific invention and innovation demonstrated by each Entrant Team throughout the Competition.



The Teams whose SocialBots attain the three highest Composite Scores during the finals event will be the winners of the Overall Performance Prizes.
Will this competition be judged like a Turing Test?
No. The goal of the Alexa Prize is to create SocialBots that engage in interesting, human-like conversations, not to make them indistinguishable from a human when compared side-by-side. While the SocialBots built for the Alexa Prize will be human-like in some respects, they will be very different in others, and could easily reveal themselves in a Turing Test. For example, SocialBots may have ready access to much more information than a human. Asking the SocialBots to act like a human could diminish the customer experience and hinder the efforts of the participants to build the best SocialBot to further conversational AI.
When and where is the finals event?
The finals event will be held in July 2023 at a location to be determined, with a science invention and innovation presentation review to follow. The competition results will be announced in August 2023.
Can we use other funding to help us participate in this challenge?
Yes, you may use other funding to support your team, subject to the terms described in the Official Rules. External funding must be disclosed to Amazon.
Will Alexa customers be able to engage with our SocialBot?
Your team will be required to submit its SocialBot for certification and publication by the Amazon Alexa team. After certification, you will enter the Internal Amazon Beta Period, where Amazon employees will test your SocialBot and provide feedback. After the Internal Amazon Beta Period, we will allow Alexa users to try your SocialBot and provide feedback to you. Amazon may impose requirements that the SocialBots must meet before they will be made available to Alexa users. Such requirements may include, among other things, a minimum average customer rating, uptime requirements, or the ability to consistently filter offensive content.
Which Alexa users will be able to interact with the SocialBots, and what languages must they support?
SocialBots will be made available to Alexa users in the United States or who select the United States as their preferred marketplace. Your team must build its SocialBot using U.S. English.
Will we publish our research from the Alexa Prize?
Yes. Publishing research papers as an outcome of your work on Alexa Prize is required for all teams participating in the competition, although teams may not publish Amazon confidential information, as described in the Official Rules. The Alexa Prize requires all teams to submit a technical paper for the Alexa Prize proceedings. Your SocialBot will not be selected for the finals if your team does not submit a technical paper for Alexa Prize proceedings. Papers will be published online at the end of the competition and made publicly available.

Teams may also publish research papers in third-party publications and conferences, as long as all papers are provided to Amazon for review at least two weeks before the submission deadlines and no research papers are published before the Alexa Prize proceedings are published, unless Amazon approves otherwise in writing.
Who will own the intellectual property rights in my submission?
You will retain ownership over your SocialBot. Amazon will have a non-exclusive license to any technology or software you develop in connection with the competition. See the Official Rules for details.
Eligibility
Who can apply to participate?
The Alexa Prize is open to full-time students enrolled in an accredited university, with the exception of universities in Cuba, Iran, Syria, North Korea, Sudan, the region of Crimea, and where prohibited by law (see Official Rules). Proof of enrollment will be required to participate.
Can I participate if I don’t attend a university?
No. The Alexa Prize is open only to full-time enrolled university students.
Do I need to be enrolled in a university program throughout my participation in the competition?
All participating team members must remain full-time students in good standing at their university while participating in the competition.
Do I need to be a certain age?
Participants must be at or above the age of majority in the country, state, province, or jurisdiction of residence at the time of entry.
Can I enroll if a family member is an Amazon employee?
Immediate family members and household members of Amazon employees, directors, and contractors are not eligible to participate. See Official Rules for additional restrictions.
Teams
How many teams will be selected to participate?
All applications will be reviewed and evaluated by Amazon. Up to ten teams will be selected and sponsored by Amazon. All teams will receive a $250,000 grant intended to support two full-time students and a month of faculty time, free Alexa devices, and free AWS hosting including access to CPU and GPU based machines, SQL and NoSQL databases, and object storage. See Official Rules for details.
How many team members can our team have?
There is no minimum or maximum number of team members. All team members must be enrolled in their university throughout their participation. All teams will receive a $250,000 grant regardless of how many members are on the team. We recommend a team with four to six students with diverse fields of study or areas of expertise.
Can students from different universities be on the same team?
No. Teams must be comprised of students attending the same university.
Can one university have more than one team?
Yes, universities may have more than one team. Multiple teams cannot have the same faculty advisor.
Can I participate on two separate teams?
No. You can only be a part of one team for the duration of the competition.
Can undergraduate and graduate students work together?
Yes, teams may be comprised of undergraduate and graduate students.
Do I need a faculty advisor?
All teams must nominate a faculty advisor and include the faculty advisor’s consent in the applications.
What is the role of the faculty advisor?
Faculty advisors will advise students on technical directions and be a sounding board for new ideas, similar to a graduate school advisor. They will also act as the official representative from the university for this competition.
Can we add or remove team members during the competition?
During the competition, there will be a period of time during which faculty advisors may request to remove or add members to the team, subject to approval by Amazon. See Official Rules for details.
Can we discuss our SocialBot with faculty or students who aren’t on our team?
Only team members may work on their SocialBots. However, the faculty advisor and other students and faculty members at your university may provide support and advice to your team and may co-author technical publications and research papers.
Application process
How do we apply?
Begin the application via YouNoodle.
What do we need to apply?
Once you have selected your team members, team leader, and faculty sponsor, you are ready to begin the application process.
Do all team members have to apply?
Each team must have a team lead, who should submit only one application on behalf of the whole team. Your application must include all of your team members’ information.
Is there an application fee?
There is no application fee.
How will teams be selected to participate?
All applications will be reviewed. Teams will be selected by Amazon based on the following criteria: (1) the potential scientific contribution to the field; (2) the technical merit of the approach; (3) the novelty of the idea; and (4) an assessment of the team’s ability to execute against their plan. Please be sure to provide enough detail in your application to enable evaluation of your proposal.
Prizes
What are the prizes for winning the competition?
Overall Performance Prize: For the three teams that build the SocialBot with the highest overall performance, the first-place team will win $250,000, the second-place team will win $50,000, and the third-place team will win $25,000. These prizes will be paid directly to the students on each winning team.


Scientific Invention and Innovation Prize: For the three teams that demonstrate the most scientific invention and innovation throughout the competition, the first-place team will win $250,000, the second-place team will win $50,000, and the third-place team will win $25,000. These prizes will be paid directly to the students on each winning team.



Grand Prize: If and only if the SocialBot of the team that wins the first-place Overall Performance Prize also achieves the grand challenge of conversing coherently and engagingly with humans for 20 minutes in at least two-thirds of its conversations at the finals event and achieves a 4.0 or higher composite score, that team’s university will be awarded a $1 million research grant.



See Official Rules for details.
Do we get a stipend and devices to participate in the Alexa Prize?
Up to ten teams will be sponsored to participate in the competition. Each sponsored team’s university will receive a $250,000 research grant to help fund the team’s participation.


The sponsorship includes Alexa-enabled devices, free AWS services to support the development of the team’s SocialBot, and support from the Alexa Prize team.
How can the grant be spent?
The grant is intended to support two full-time students for the duration of the competition and one month of the faculty advisor’s salary. No more than 35% of the research grant may be allocated to administrative fees. If your team would like to use the funds in another manner, your faculty advisor must receive approval from Amazon before doing so.
How will the prizes be distributed among a team?
Each Overall Performance Prize and the Scientific Invention and Innovation Prize will be distributed equally among the members of each winning team.
Timeline
What are the key milestones of the competition?
Teams must submit their applications by October 5, 2022. Teams selected to participate in the competition will be notified in October of November 2022. The competition will run from about November 2022 through August 2023. See Official Rules for details.

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US, WA, Seattle
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Member of Technical Staff, Frontier AI & Robotics (FAR)
US, CA, San Francisco
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Member of Technical Staff, Frontier AI & Robotics (FAR)
US, CA, San Francisco
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Applied Scientist II, SSG Science
IN, KA, Bengaluru
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Applied Scientist II, Search Ranking, Search Ranking
US, CA, Palo Alto
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Applied Scientist II, Computational Modeling, OMHS SCS
US, MA, Boston
Are you excited about applying machine learning and applied mathematics to real-world systems at massive scale? As an Applied Scientist on this newly formed team, you will collaborate closely with scientists and engineers to bring research into production across a broad portfolio of problems — from computer vision perception platforms to building-wide optimization and orchestration. You will frame ambiguous business problems as tractable scientific challenges and implement novel machine learning (ML) systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments. This is a ground-floor opportunity to shape the scientific direction of a new organization, where your contributions will directly influence how Amazon's fulfillment network operates and evolves. Key job responsibilities - Design, develop, and deploy ML and scientific solutions spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed modeling in production environments. - Rapidly ramp on unfamiliar problem domains, frame ambiguous business problems as tractable scientific challenges, and prototype solutions end to end. - Author or co-author research findings for internal or external peer-reviewed venues, and provide peer feedback on research procedures and results across teams. - Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints. - Collaborate across multiple science and engineering teams to integrate your solutions into deployment architecture, mentoring less experienced scientists along the way. A day in the life You might start your morning reviewing experiment results from an overnight model training run, then shift into a design discussion with engineers on how to deploy a new computer vision model to edge hardware in a fulfillment center. After lunch, you could be prototyping a physics-informed optimization approach, writing up findings for a research paper, or pairing with a teammate to debug a tricky data pipeline. As part of a new and growing organization, you will have a direct hand in shaping team practices, scientific roadmaps, and the tools you use every day. About the team Our team sits within Amazon's fulfillment technology organization and applies a range of scientific disciplines — including computer vision, optimization, reinforcement learning, and statistical modeling — to improve how goods move through Amazon's global fulfillment network. We build the models and systems that drive real-time orchestration, optimizing throughput, flow, and operational performance at scale. As a newly formed organization, we are building our culture and scientific agenda from the ground up. You will join a collaborative, inclusive group of scientists and engineers who value experimentation, rigorous research, and delivering measurable impact for customers.
Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business
CN, 31, Shanghai
Team & Project Overview The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces. Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement. Scope of Impact Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE) Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC) Influence $100M+ annual seller GMS through improved segmentation and contact optimization Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
Sr. Applied Scientist, SSG Science
IN, KA, Bengaluru
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities What will you do? - Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms - Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques - Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine - Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics - Train custom Gen AI models that beat SOTA and paves path for developing production models - Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices - Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys.
Applied Scientist, SSG Science
IN, KA, Bengaluru
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities What will you do? - Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms - Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques - Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine - Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics - Train custom Gen AI models that beat SOTA and paves path for developing production models - Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices - Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys.
Applied Scientist, Prime Video Commerce Insights
GB, London
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies — all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business — available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video, in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through checkout and beyond. We operate at scale across hundreds of millions of customers, and we are expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team in London. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a delivery focused role within a high-visibility multidisciplinary group of engineers and scientists, focused on improving the customer experience for Prime Video. Key job responsibilities - Research, design, and implement machine learning approaches (e.g. reinforcement learning and recommendation systems) that personalise across different customer touch points. - Collaborate with engineers to deploy and integrate successful experiment results into large-scale, complex Amazon production systems with low latency. - Design and execute rigorous experiments to demonstrate the technical efficacy and business value of your methods. - Act as a subject-matter expert and help define the science roadmap and research agenda in line with organisational priorities and production constraints. - Provide machine learning thought leadership to technical and business leaders, thinking strategically about business, product, and technical challenges. - Work with technical product managers to work backwards from what matters to customers and deliver ML-backed solutions. - Share results with the team and wider scientific community through documents that are both statistically rigorous and compellingly relevant. A day in the life You will be a research leader and innovator within the Commerce Insights organisation. You will collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines, balancing competing objectives, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer-experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.
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