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.

Latest news

The latest updates, stories, and more about Alexa Prize.
  • knowledge-graph.png._CB453975899_.png
    Innovations from the 2018 Alexa Prize
    Behnam Hedayatnia
    March 5, 2019
    The 2018 Alexa Prize featured eight student teams from four countries, each of which adopted distinctive approaches to some of the central technical questions in conversational AI. We survey those approaches in a paper we released late last year, and the teams themselves go into even greater detail in the papers they submitted to the latest Alexa Prize Proceedings. Here, we touch on just a few of the teams’ innovations.
    Conversational AI
  • CoBot_architecture.jpg._CB453492855_.jpg
    AI tools let Alexa Prize participants focus on science
    Anushree Venkatesh
    February 27, 2019
    To ensure that Alexa Prize contestants can concentrate on dialogue systems — the core technology of socialbots — Amazon scientists and engineers built a set of machine learning modules that handle fundamental conversational tasks and a development environment that lets contestants easily mix and match existing modules with those of their own design.
    Conversational AI

Work with us

See more jobs See more jobs
Data Scientist, Phased Array Antenna System Validation
US, WA, Redmond
Amazon LEO is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. About this role We're looking for a Data Scientist to turn data from our satellite fleet into engineering decisions. Every satellite in orbit sends back a steady stream of telemetry. Every unit was also tested heavily on the ground before launch, and the ground segment watches the satellite every day while it operates. Your job is to connect these sources. You'll find out which in-orbit behaviors trace back to what we measured in the factory, which events repeat across the fleet, and which patterns point to weaknesses in the hardware design. Your findings will directly shape how we design, build, test, and operate the next generation of satellites. A typical problem starts small: a handful of satellites show a drifting temperature, an occasional link dropout, or a counter that creeps up faster than expected. You'll work out whether it's a real effect or noise, whether it's spreading, and what the affected units have in common. They might share a manufacturing lot, a configuration, an orbital environment, or a signature in their factory test history. From there you'll form a hypothesis about the physical cause and argue it in front of hardware, firmware, and reliability engineers who will push back hard. You'll also have to say plainly when the data rules your own idea out. Telemetry alone rarely settles the question, so your first stop is the ground test data we already have. Every unit in orbit has a detailed record from manufacturing and environmental testing, and the fleet as a whole is backed by an even larger population of units still on the ground. You'll dig through that history for the same behavior: did the affected units show early signs on the ground, did they sit near a test limit, does the effect appear across many units under certain conditions? Often the answer is already in data no one has looked at in this light. When it isn't, you'll design the ground experiments that can provide it. That means deciding which units to test and under what conditions, how many runs you need before the result means anything, and what outcome would confirm or refute the theory. You'll work with the engineers who run those tests, analyze the results, and close the question with a clear answer and a recommended action. Beyond individual investigations, you'll build the fleet-level view: baselines for healthy behavior, early warning indicators that catch emerging issues before they cause outages, and reliability estimates that tell us how the population is aging. You'll decide which recurring analyses should become automated monitoring and work with software engineers to make that happen. You'll be the person the team turns to when someone asks "is this normal?" about the fleet. The ideal candidate is curious about how hardware fails and comfortable with messy data from many sources. They can turn "we're seeing something odd on a few satellites" into a rigorous analysis and a clear recommendation. What you'll bring: - Master's degree or above in a quantitative field such as statistics, mathematics, data science, physics, electrical or aerospace engineering, or computer science, or PhD - 3/5+ years of hands-on experience in an applied or industry setting using Python and SQL to analyze large datasets such as time series, telemetry, sensor, or test data - Experience applying statistical methods (hypothesis testing, regression, time-series analysis, anomaly detection) to noisy, incomplete real-world data - Experience building visualizations and dashboards that communicate findings to engineers and decision makers - Experience framing and solving loosely defined problems, from the first question to a recommendation people act on - Ability to work with hardware and software engineers and turn their questions into analyses you can actually run Nice to have: - Experience analyzing telemetry from satellites, spacecraft, aircraft, vehicles, or other fleets of deployed hardware - Experience with reliability and failure analysis methods such as survival analysis, Weibull modeling, failure-mode analysis, and root-cause investigation - Familiarity with electronics or RF hardware, manufacturing test, environmental stress screening (thermal cycling, burn-in, HASS/HALT), or yield analysis - Experience with pattern-discovery techniques such as clustering, change-point detection, and unsupervised anomaly detection - Experience with large-scale data tools (Spark, Athena/Presto, Redshift) and AWS - Experience with visualization tools such as QuickSight, Tableau, Grafana, or Plotly/Dash - Experience working with software engineers on data pipelines: data requirements, schema design, data quality checks, and automating recurring analyses - Experience using AI-assisted tools to speed up analysis and automate repetitive work - A track record of changing engineering design or test decisions with data Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities As a Data Scientist, you will… - Analyze in-orbit telemetry to measure the health and performance of satellite subsystems, and detect anomalies and slow degradation early - Correlate in-orbit behavior with pre-launch data, including manufacturing, production test, and environmental test results, to find which ground measurements predict on-orbit performance. Feed those insights back into test limits and screening strategy - Correlate in-orbit data with what the ground segment sees (gateways, customer terminals) and with failure analysis of lab and returned units - Connect events across the fleet by build lot, component, configuration, firmware version, orbit, and operating conditions, so we can tell systemic issues apart from one-off events - Find patterns that point to hardware design weaknesses, measure their impact on the fleet, and present the evidence to hardware engineering teams with clear recommendations - Build visualizations and dashboards that support decisions in fleet health reviews, anomaly investigations, and design reviews - Work with software engineers to improve the data pipeline: define data requirements, improve data quality, link ground and in-orbit datasets, and automate recurring analyses - Own ambiguous investigations end to end, from forming the hypothesis to reporting the outcome to senior engineering leaders About the team Phased Array Antenna System Validation is the team that decides whether a satellite's phased array antennas (PAAs) are ready to fly. These are the electronically steered transmit and receive panels that form the link between the satellite and the ground. We're part of Amazon Leo, Amazon's low Earth orbit satellite constellation, and we own the test strategy, the automation, and the data pipeline that every PAA goes through on its way to orbit. That path runs from early component-level screening through full-panel functional testing and extended environmental stress testing across temperature, vacuum, and vibration. Our responsibility doesn't end at launch. We also own the analysis of telemetry from PAAs in orbit, and we use what the constellation tells us to improve how we test on the ground. We are a hardware-facing software and test team, and we sit where several disciplines meet. Our test automation directs firmware provisioning, self-tests, and environmental profiles on test stations at manufacturing sites. Over a single run it exercises thousands of measurement points: high-speed serial links, clocking, digital beamformer firmware, RF chains, power, and thermal behavior. Every result is tied to a specific serial-numbered part. That builds a detailed, part-level history of how each panel and component behaved under stress, from the factory floor to launch. A large part of our work is investigation. When something fails, we work out whether the cause is the hardware, the firmware, the test equipment, or our own automation, and we decide what should happen next. Over time we have built a growing catalog of failure signatures and their root causes, internal tools for analyzing long-duration tests, and AI-assisted agents that help turn raw logs into conclusions. We care about evidence over assumption. A label on a result isn't the result itself, and a search that finds nothing doesn't prove the problem has no history. Theories get tested against the data before they're accepted. We support multiple hardware generations at once, so the architecture, the questions, and the tooling keep changing. You'll work closely with antenna hardware, firmware, manufacturing, and reliability engineers who are glad to dig into a hard problem with you. Because we own both ends, from the first ground test to behavior in orbit, the loop is closed within the team. What we learn from the constellation goes straight back into our screens, thresholds, and test sequences, so that the next panel we validate is more likely to keep working in orbit.
Applied Scientist , Fauna
US, NY, New York
We are seeking an Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion systems for our intelligent robots. In this role, you will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging and dynamic environments. You will own the full pipeline from algorithm development through embedded deployment, ensuring that perception systems run efficiently on resource-constrained robotic hardware. You will also leverage modern machine learning approaches to push the boundaries of classical perception methods, combining learned representations with geometric techniques to achieve robust, real-time performance. This is a deeply hands-on role. You will work directly with sensors, hardware, and real-world data, while prototyping, testing, and iterating in physical environments. The ideal candidate has strong foundations in VIO and sensor fusion, practical experience optimizing algorithms for embedded platforms, and familiarity with how modern deep learning is transforming perception. Key job responsibilities - Design and implement Visual Inertial Odometry algorithms for robust real-time state estimation on robotic platforms like Sprout - Develop multi-sensor fusion pipelines integrating cameras, IMUs, and other sensing modalities for accurate pose tracking - Optimize perception and tracking algorithms for deployment on embedded hardware (e.g., ARM, GPU-accelerated edge devices) under strict latency and power constraints - Apply modern ML-based perception techniques (learned features, depth estimation, neural odometry) to complement and improve classical geometric approaches - Build and maintain calibration, evaluation, and benchmarking infrastructure for perception systems - Collaborate with hardware, controls, and navigation teams to integrate perception outputs into the robot’s autonomy stack - Lead technical projects from research prototyping through production deployment
Senior Applied Scientist, Leo Space Security
US, WA, Bellevue
Amazon Leo is a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband network connectivity to unserved and underserved communities around the world. We are looking for an Applied Scientist to be a founding scientist on the Engineering and R\&D team within Leo Infrastructure and IP Security. The team defends the manufacturing lines, launch sites, and global ground infrastructure behind the constellation from the most sophisticated threat actors on the planet. These requirements create open scientific problems at the intersection of agentic AI, real-time stream processing, graph-based reasoning, and behavioral analytics. You will build the science behind a neurosymbolic reasoning platform and the models that detect the behavior of sophisticated threat actors. This is an R\&D role with a production mandate, where you define the problem rather than solve a pre-scoped one, and every model, detection, and agent workflow you build becomes the system Leo's security teams use to protect the constellation. #### Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen. Key job responsibilities - Design and implement scalable, production-grade neurosymbolic systems that integrate symbolic reasoning over graph-based knowledge representations with LLM agents to deliver reliable, verifiable security outcomes. - Design and run reinforcement learning and fine-tuning pipelines (GRPO, PPO, DPO) to optimize language models for security reasoning, triage, and detection-authoring tasks. - Build behavioral and statistical models that detect threat actor behavior, and design the evaluation frameworks that measure model performance against that behavior before trusting a model in production. - Design and build multi-agent systems that autonomously triage, enrich, and contain security events, including the constrained reasoning, safety guardrails, and validation mechanisms that make automated decisions trustworthy at scale. - Own the end-to-end science lifecycle, from research and experimentation through production deployment, defining the metrics that measure system performance and real-world security impact. - Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, and shape the scientific agenda and research culture from day one. A day in the life You will move between research and production in the same week: framing an ambiguous security problem as a scientific question, prototyping an approach, and partnering with software engineers to ship it as a capability the platform runs continuously. Security engineers on your team translate threat intelligence into the adversary behaviors that matter; you build the models that detect those behaviors and evaluate model performance against them. You will obsess over the two latencies that define the platforms, the time from event to detection and the time from detection to containment action, and design agents and detections that drive both down. You will backtest candidate detections against retained telemetry, review evaluation results before a model or agent capability graduates to automated execution, and deliver scientific artifacts that ship. About the team Leo Infrastructure and IP Security protects the people, facilities, hardware, and supply chain behind a global satellite constellation. The Engineering and R&D team within this organization builds the platforms and tooling the security pillar teams operate on, moving security operations from manual triage to correlation-based detection, automated response, and agentic AI. The team is composed of applied scientists, software engineers, and security engineers working across physical and digital security domains. #### Inclusive Team Culture In Amazon Security, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. #### Training & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. #### Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
Research Scientist II, Grocery ECO
IN, KA, Bengaluru
We are looking for a talented Research Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As a Research Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency.
Applied Scientist, Fauna Robotics
US, CA, Sunnyvale
Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
Sr. Economist, WW Prime
US, WA, Seattle
Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company.
Applied Scientist II, Sponsored Products and Brands
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. As an Applied Scientist within the Brand Intelligence team, you will be responsible for improving the quality of underlying agentic solutions and their impact within the ads delivery stack. We are looking for a passionate candidate with technical expertise in agentic systems, continual learning, evaluation, information retrieval, Natural Language Processing (NLP), and Large Language Models (LLM). In addition to having hands-on experience in building ML-based solutions, an ideal candidate should be able to create and articulate a customer-centric science vision, show willingness to continuously learn about new scientific approaches, and enjoy operating in startup-like environment.
Applied Scientist, AGI
US, CA, Sunnyvale
Amazon is looking for an Applied Scientist with a strong deep learning background to help build the training environments that teach large language models to reason, use tools, and complete complex, multi-step tasks. Key job responsibilities You will design, build, and evaluate reinforcement learning environments (RL gyms) for LLM training: realistic, verifiable task environments where models learn through interaction and feedback. This includes defining tasks and reward signals, building the infrastructure to run environments at scale, measuring how well environments transfer into model capability, and developing rigorous methods to verify environment quality and correctness. You will work closely with model training teams to understand where environments are most valuable and iterate based on what the models learn. Your work will directly shape products and services that use generative AI. About the team The Foundational AI (FAI) team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.
AI Engineer - RL Manipulation, RIVR
CH, Zurich
Are you passionate about pushing the boundaries of what robots can do? Amazon is looking for a creative and driven Applied Scientist to help build intelligent robotic manipulation systems powered by reinforcement learning. In this role, you will research, design, and deploy machine learning solutions that enable robots to interact with the physical world in increasingly capable ways. You will tackle complex, ambiguous problems where textbook answers don't exist, working backwards from real-world needs to invent new approaches. Your work will directly influence how automation systems learn, adapt, and perform at Amazon's scale — and you will see your research come to life on real hardware. Key job responsibilities - Research and develop reinforcement learning methods for robotic manipulation, advancing the state of the art in sim-to-real transfer, contact-rich control, and adaptive grasping strategies. - Design, implement, and evaluate machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch or TensorFlow to train policies that generalize across varied manipulation tasks. - Build and maintain production-quality code that integrates scientific prototypes into larger robotics systems, making thoughtful tradeoffs between complexity, performance, and maintainability. - Collaborate with cross-functional engineering and science teams to define simulation requirements, validate model performance, and iterate on solutions informed by real-world hardware feedback. - Contribute to the team's scientific roadmap by identifying new research directions, mentoring fellow scientists, and publishing results at peer-reviewed venues when appropriate. A day in the life You might start your morning reviewing overnight training runs, analyzing reward curves and failure modes from your latest reinforcement learning experiments. After a quick sync with robotics engineers, you shift to refining a simulation environment to close a gap you spotted between sim and real performance. In the afternoon, you pair with a teammate to design a new evaluation framework, then wrap up by drafting a short write-up of your findings to share with the broader team. About the team Our team sits at the intersection of machine learning research and real-world robotics, focused on teaching robots to manipulate objects with precision and adaptability. We combine reinforcement learning, physics-based simulation, and hands-on hardware validation to deliver solutions that work beyond the lab. We value intellectual curiosity, collaborative problem-solving, and a shared commitment to turning ambitious research ideas into tangible impact. If you are excited about building intelligent systems that learn from experience and operate at scale, this is the team for you.
Economist, Entertainment Economics
US, WA, Seattle
The charter of the Entertainment Economics team includes working on high-impact projects that require economics expertise and a cross-business perspective. Our team supports Amazon’s diverse entertainment businesses – especially Prime Video, Amazon Music, Amazon MGM Studios, and Gaming (Luna) – through complex causal measurements (observational and experimental) and structural modeling. Some of our team’s areas of science expertise include marketing science, long-term value measurement, and experimentation. As a central economics team not embedded in any particular business, we strive to provide objective analyses that take a customer-obsessed, pan-Amazon perspective. This role partners with the Prime Science team to evaluate and improve the way Amazon allocates Prime subscription revenue across its benefit teams, including entertainment businesses. Prime is Amazon's premiere membership program, with over 200mm members world-wide, bringing in billions in Prime subscription revenue annually. Prime is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits: fast and free shipping, entertainment & digital content, reading, healthcare, and grocery. Each of these Prime benefits is supported by a different organization, and each relies on Prime subscription revenue to make critical investment decisions. Our team partners with Prime Science to ensure that these allocations are credible, actionable, and incentive compatible for every benefit. To support this work, this role will create science models, validate them, and productionize them. In addition to stellar economics expertise, this role requires systems thinking (anticipating how complex interconnected models will respond to changes in data or modeling), strategic thinking (supporting decisions that affect 200 million+ customers and set Amazon’s direction for years to come), and scientifically novel thinking (using AI and the latest science advancements to achieve move). The ideal candidate is expert in causal inference with experience working in large, complex structural modeling environments. They are a creative problem-solver, able to identify new approaches that solve multiple problems at once while minimizing churn. They think big, not limiting themselves to what’s been done before, while tactfully accounting for the costs of major changes. They can add structure to incredibly ambiguous business contexts, making defensible assumptions and delivering measurements that drive impactful business decisions. They have effective communication and the skill to translate data-driven findings into actionable insights for senior leaders. Finally, they are empathetic and constructive. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help leaders ask the right questions, and then deliver data-driven answers, helping to create the future of Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across entertainment and Prime • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social