Social

SimBot Challenge FAQs

Frequently asked questions about the challenge.
General
General
What is the Alexa Prize?
Alexa is Amazon’s cloud-based voice service available on over 100 million devices from Amazon and third-party device manufacturers. With Alexa, you can build natural voice experiences that offer customers a more intuitive way to interact with the technology they use every day. Our collection of tools, APIs, reference solutions, and documentation makes it easy for anyone to build with Alexa.
Why did Amazon create the Alexa Prize?
The Alexa Prize, an annual university competition dedicated to accelerating the field of conversational artificial intelligence (AI), was created to recognize students from around the globe who are changing the way we interact with technology. The goal 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.
How does the Alexa Prize support research?
The Alexa Prize is a research testbed for university students to experiment with and advance conversational AI at scale.

Research teams own the intellectual property (IP) in their systems and are encouraged to publish scientific articles on their work. As described in the Official Rules, participating teams grant Amazon a non-exclusive license to any technology or software they develop in connection with the competition.
What new datasets will I have access to as part of the Alexa Prize?
The TEACh dataset as well as a training dataset proprietary to the live interaction portion of the SimBot Challenge will be available to competitors in each phase.
SimBot
What is the SimBot Challenge?
SimBot, a competition focused on helping advance development of next-generation virtual assistants that will assist humans in completing real-world tasks by continuously learning, and gaining the ability to perform commonsense reasoning.

The SimBot Challenge will have two phases: A public benchmark phase, and a live interactions phase. Participants in both phases will build machine-learning models for natural language understanding, human-robot interaction, and robotic task completion. Artificial intelligence challenges addressed in the competition relate to reasoning on language and scene understanding, learning from demonstration, self-learning, and task completion utilizing natural language.

Unlike previous Alexa Prize competitions, the public benchmark challenge phase will be open to university teams, as well as individuals in academia and industry interested in advancing the science of AI and engaging top researchers from around the globe. The SimBot Challenge public benchmark phase is like existing visual language navigation competitions.
What is the difference between the Public Benchmark Challenge and the Live Interaction Challenge?
The Public Benchmark Challenge is open to any individual or team, academic or industry, who wants to complete and submit a model for evaluation and rating during the evaluation period. It is based on the TEACh dataset offered to the research community in October, 2021. The Live Interaction Period’s participation is limited solely to the university-based teams selected for the SimBot Challenge in November 2021 and June, 2022. These teams will each receive Amazon sponsorship to build a SimBot that will compete in a challenge from July, 2022 to September 2022 where they will receive real time ratings and feedback from Alexa Users.
Why is the SimBot - Live Interaction Challenge only limited to university teams?
The SimBot Challenge - live interaction phase is part of the Alexa Prize which is currently limited to universities and university students in Amazon initiatives to advance AI. However, the Public Benchmarking Challenge is open to both academic and university-based teams.
What will my SimBot do?
SimBot will navigate in a virtual environment to complete challenges guided by Alexa users. SimBot will interact with objects in the environment including but not limited to those common to offices and homes. There will be obstacles and hazards introduced to make game play fun and challenging.
How will I build my SimBot?
SimBots will use images from the game and instructions from Alexa users to navigate in a virtual world. Teams will build AI models which recognize objects and scenes as well as understand natural language commands from users. We plan to provide baseline data and a baseline model as a reference but we expect teams to develop new and novel approaches as well as augment the provided data to improve performance.
Eligibility: Public Benchmark Challenge
Phase 1, SimBot challenge
Who is eligible to participate in the public benchmark challenge?
Any individual or team, academic or industry-based.
How do I sign up to participate in the public benchmark challenge?
A registration form will open on November 15, 2021 at alexaprize.com.
Is funding available to university teams competing in the public benchmark challenge?
No, funding is only available to university teams selected to participate in the SimBot Challenge. In addition to the teams selected in 2021 up to four new, high-performing teams from the public benchmark challenge will be invited to apply for the SimBot challenge, live interaction phase for which funding is available. Learn more here.
Who can apply to participate?
Any individual or team can register and participate in the public benchmark phase with exception of parties from Cuba, Iran, North Korea, Sudan, Syria, and the region of Crimea.

Registration for the public benchmark challenge will open November 15, 2021. The challenge will begin on January 10, 2022.
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 registration.
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.
Eligibility: SimBot Challenge
Includes sponsored teams for both Public Benchmark and Live Interaction phases
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, North Korea, Sudan, Syria, and the region of Crimea (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 the duration of 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.
If my team fails to apply for the SimBot Challenge now, will there be any future opportunities to compete in the Live Interaction challenge?
Yes, the initial application period run from October 4-31, 2021. In addition to the teams selected in 2021 up to four new, high-performing university teams from the public benchmark challenge will be invited to apply for the SimBot challenge, live interaction phase for which funding is available. Learn more here.
Will university teams selected post-Public Benchmark Challenge be eligible for funding and for what amount?
Teams selected for the SimBot Challenge in June, 2022 will be eligible for funding.
If my team was not selected during the first application period are we eligible to re-apply in may if we qualify?
Yes. University-based teams that participate in the public benchmark phase are eligible to re-apply for the SimBot challenge between May 9 - 23, 2022. Four new, high-performing university teams from the public benchmark challenge will be invited to apply for the SimBot challenge, live interaction phase for which funding is available. Learn more here.
My team is not completely comprised of university students but we perform well in the public benchmark challenge? Is there an opportunity for us to continue in the competition?
Yes, we plan to continue the public benchmark competition through the end of 2022.
My company does collaborative work with several universities, are we eligible to compete in the live interaction challenge alongside them?
No, only full-time students are eligible to participate in the SimBot challenge, which includes the live interaction period.
My university and another frequently collaborate on projects - is there any exception to the students all enrolled in one university rule?
Applications are limited to students all from the same, single, university.
Teams
How many teams will be selected to participate?
All applications will be reviewed and evaluated by a panel of Amazon experts. Up to ten teams will be selected and sponsored by Amazon. All teams selected in November, 2021 will receive a $250,000 grant while those selected in June, 2022 will receive a $200,000 grant intended to support two full-time students, 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 the Official Rules for details.
How many team members can our team have?
There are no minimum or maximum team member requirements. All team members must be enrolled in their university throughout the duration of the competition. All teams will receive a $250,000 grant if selected in November 2021 or a $200,000 grant if selected in June, 2022 regardless of how many members are on the team. We recommend a team with 4-6 students with diverse fields of study or areas of expertise.
Can students from different universities be on the same team?
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.
Can I participate on two separate teams?
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, faculty advisors may request to remove or add members to the team, subject to approval by Amazon.
Can we discuss our SimBot with faculty or students who aren’t on our team?
Only team members may work on their SimBot. 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?
Check the SimBot page for the latest update on applications.
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 apply 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 by a panel of Amazon employees. Teams will be selected 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 our experts to evaluate your proposal.
Competition details
What is the goal of the challenge?
To develop AI models which advance the state of the art and allow users to naturally interact with a robotic assistant in a virtual world to successfully complete a range of challenges.
How will winners be selected?
Winners will be determined based on the final standings at the completion of the finals period.
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 will need to be disclosed by January 1, 2023.
Will Alexa customers be able to engage with our SimBot?
Your team will be required to submit its SimBot 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 SimBot and provide feedback. After the Internal Amazon Beta Period, we will allow Amazon Alexa customers to try your SimBot and provide feedback to you. Amazon may impose Availability Criteria, or requirements the SimBot must meet before it will be made available to Alexa users. Availability Criteria may include criteria such as a minimum average customer rating, uptime requirements, and an ability to consistently filter offensive content.
Amazon launched the Echo in the UK, Germany, India, Japan, and other countries. Will localized languages be supported?
Your team must build its SimBot using U.S. English. Your SimBot will be available to Alexa customers in the U.S. Customers in other countries may also access it by setting their Amazon PFM (Preferred Marketplace) to U.S.
Will we publish our research from the Alexa Prize?
Yes. Publishing research papers as an outcome of your work on the Alexa Prize is required for all teams participating in the competition, although teams should 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 SimBot will not be selected for the finals if your team does not submit a technical paper for Alexa Prize proceedings. Papers will be published at the end of the competition in an online Proceedings of the Alexa Prize, which will be 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 without Amazon’s prior approval.
Who will own the intellectual property rights in my submission?
You will retain ownership over your SimBot. 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.
Prizes
What are the prizes for winning the competition?
A prize of $500,000 will be awarded to the team that creates the best SimBot. The second-place and third-place finalist teams will receive a $100,000 and a $50,000 prize, respectively. See the contest 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 Alexa Prize in November, 2021. These teams’ universities will receive a $250,000 research grant to fund the team members’ work over the year. Additional teams selected to participate in the SimBot Live Interaction challenge in June, 2022 will receive a $200,000 research grant to fund the team members’ work over the year.

The sponsorship includes one Alexa-enabled device per team member for up to a total of three devices per team and one Alexa-enabled device per faculty advisor, free AWS services to support the development of their SimBot, and support from the Alexa team.
How can the grant be spent?
The grants will be awarded with the intention that they will 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 grants 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.
What happens if we are selected and receive a stipend but can no longer participate?
Stipends will be awarded in installments payable to the university. If your team withdraws before any of the installments, remaining funds will not be transferred to the university.
How will the prizes be distributed among a team?
The first, second, and third place prizes will be distributed equally among all registered team members. The official list of registered team members must be confirmed by January 30, 2023.
Timeline
What are the key milestones of the competition?
Teams must submit their applications between October 1, 2021 and October 31, 2021. Between November 1 and November 10, 2021, we will announce teams selected to participate. In the summer of 2022 following the public benchmark phase and the second application period teams will be invited to an Alexa Prize Bootcamp at Amazon where they will receive training on the resources made available to all competing teams. The finals will be scheduled between January 30 and March 27, 2023 and will determine the winning teams in the first annual SimBot Challenge.
If selected, when will we receive the stipend, devices, access to the Alexa Prize SimBot toolkit, our AWS account, and be introduced to our point of contact?
We will reach out to all teams no later than November 10, 2021 with instructions on next steps. Up to ten teams will be selected to receive a $250,000 stipend, Alexa-enabled devices, free AWS services to support their development efforts, and support from the Alexa team.
More information
See the full competition rules or submit your questions. Need assistance? Email: alexaprizesupport@amazon.com

Latest news

The latest updates, stories, and more about Alexa Prize.
Load more

Work with us

See more jobs See more jobs
Senior Applied Scientist, Amazon Connect
US, NY, New York
Are you excited about applying machine learning and statistical modeling to real-world systems that serve millions of customers? Amazon Connect is a cloud-based contact center service that helps businesses deliver personal, efficient customer experiences. Our team of scientists and engineers builds the AI and ML capabilities that power contact center operations and optimization. We are looking for a Senior Applied Scientist to tackle scientifically complex challenges in areas such as stochastic modeling, queueing theory, anomaly detection, and optimization. In this role, you will design and deploy novel ML models and algorithms that directly improve how businesses interact with their customers. You will work at the intersection of research and production, turning ambiguous problems into scalable solutions that shape the future of cloud-based customer service. Key job responsibilities - Design and deploy novel machine learning models and algorithms to solve complex problems in contact center operations, including forecasting, routing optimization, and anomaly detection. - Lead the scientific agenda for your team by identifying new research opportunities, proposing initiatives, and driving them from concept through production deployment. - Collaborate with engineering teams to architect and implement scalable ML systems, personally contributing significant portions of the critical scientific components. - Mentor fellow scientists and engineers through code reviews, design discussions, and scientific guidance, raising the overall technical bar of the team. - Evaluate and advance the team's ML methodology by benchmarking against current academic and industry research, and by publishing findings internally and externally when appropriate. A day in the life You might start your morning reviewing experiment results from a new forecasting model, then join a design session with engineers to discuss how to integrate it into the production pipeline. After lunch, you could be whiteboarding a novel approach to a queueing optimization problem with a fellow scientist, followed by a code review for a teammate. You will regularly present your research findings to stakeholders across the organization and contribute to the team's publication efforts. About the team Our team within Amazon Connect focuses on building intelligent, ML-driven capabilities that help businesses run their contact centers more effectively. We work closely with product, engineering, and science partners to turn research ideas into features that customers rely on every day. We value curiosity, collaboration, and scientific rigor, and we are investing in new AI capabilities that will continue to transform the customer service industry. If you want to see your research make a tangible impact at scale, this is the place to do it.
Applied Scientist I, Amazon Shipping
IN, HR, Gurugram
Building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. Key job responsibilities 1. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 2. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. 3. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 4 Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 5. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing.
Applied Scientist, Customer360
US, WA, Seattle
What happens when you give AI the ability to remember? Not cached responses — real structured memory that compounds over time and transfers across contexts. We're building the science behind this, and we need researchers who want to own the problem end-to-end. This is a founding role on a new team. You won't inherit models or maintain someone else's pipeline. You'll define the research direction, run experiments at scale, and ship what works directly to production. Key job responsibilities As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for knowledge acquisition and retrieval. You will adopt or invent new machine learning and analytical techniques in the realm of information retrieval, knowledge representation, and large language models. Specific responsibilities include: 1. Design and implement novel approaches to knowledge extraction from heterogeneous, unstructured data sources at organizational scale. 2. Build retrieval systems that match intent to relevant knowledge across domains — solving the "right memory at the right time" problem. 3. Own the quality of memory generation: what to capture, how to structure it, when to surface it, and when to let it decay. 4. Run large-scale experiments using Amazon's compute infrastructure and massive real-world datasets. 5. Develop evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level. 6. Collaborate with engineers to move from research prototype to production system in weeks, not quarters. 7. Invent new approaches to temporal knowledge management — how memories age, conflict, and compound over time. 8. Publish and patent novel approaches to knowledge acquisition and retrieval at top-tier venues. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team We're a new team within Personalization, focused on a different kind of recommendation: not "what product should this customer see" but "what knowledge should this AI use right now." Same scale, same rigor, entirely new problem space. The science is at the intersection of information retrieval, knowledge representation, and LLM reasoning — and the right approach hasn't been established yet. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.
Applied Scientist, SCOT FO - SnT
ES, B, Barcelona
How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network? SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting. • You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars: • Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization, while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally. • Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems. Basic qualifications • PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) • Strong programming skills (Python preferred; experience with optimization solvers a plus) • Research experience in one or more: • Large-scale mathematical programming (LP, MIP, decomposition methods) • Combinatorial optimization (assignment, scheduling, network flows) • Multi-objective optimization and control • Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification) • Causal inference (spatiotemporal causal modeling, offline policy evaluation) Preferred qualifications • Experience building optimization systems that run in production at scale • Being comfortable with ambiguity and fast iteration cycles • Publications in relevant venues Key job responsibilities Design and implement optimization and forecasting models for large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity, accuracy) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization or forecasting problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.
Data Scientist II, Device Economics
US, CA, Sunnyvale
We are seeking Data Scientist II with strong science application skills to join our Device Economics team. This role will focus primarily on Amazon's innovative devices and services (e.g. Echo Family of Devices), working at the intersection of economic modeling, forecasting science, and business strategy. The ideal candidate will be responsible for pre-launch forecasts, annualized overall forecasts, identifying substitution patterns, and partnering closely with product managers and marketing managers to understand the evolution of the Devices portfolio. Key job responsibilities Forecasting & Modeling 1. Develop and maintain pre-launch forecasts and annualized overall forecasts for Amazon Devices 2. Identify and model substitution patterns across the device portfolio 3. Build economic and financial models to support demand planning and business decisions 4. Formulate relevant analytical frameworks to address key economic issues in device forecasting Science Communication & Collaboration 1. Explain complex science models and methodologies to non-technical stakeholders including product managers and marketing managers 2. Collaborate with economists, data scientists, and applied scientists across Decision Science 3. Present results of analyses to cross-functional teams and leadership 4. Build trust in science models and forecast outputs with product teams Innovation & Strategic Thinking 1. Think creatively about ways that leading-edge analytics and emerging data sources can address Devices' most pressing business challenges 2. Help internal teams leverage analytic tools to better manage innovation 3. Conduct empirical studies and perform quantitative and qualitative research 4. Identify opportunities to improve forecasting accuracy and business impact Cross-Functional Partnership 1. Work closely with product managers and marketing managers to understand portfolio evolution and business strategy 2. Support DSO leadership in quarterly business reviews and strategic planning A day in the life Your days will be split between refining and building models and working with business leaders to interpret them. You own science-based forecasts that can directly impact Amazon's bottom line on the order of multi-million dollar decisions. - You will perform model refreshes or updates to analyses as needed; and, - You will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. About the team The Decision Science team within DSO (Device Supply Organization) is responsible for forecasting and demand planning initiatives across Amazon Devices. The DSO team of 300+ engineers, scientists, and PMs applies quantitative methods and data-driven approaches to replace judgment-based decisions with science-driven forecasts. Decision Science focuses on lifetime demand forecasting using econometric and machine learning models for rapid reforecasting, mix adjustments, and portfolio management for new product launches. We also inform to go/no-go investment decision for new product initiatives
Applied Scientist II, Amazon Supply Chain
US, WA, Seattle
As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. We are looking for an Applied Scientist to join our team that is building enterprise applications leveraging machine learning, generative AI, and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive, differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated, turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers. As an Applied Scientist, you will design and develop machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact, translating scientific advances into production systems that serve millions of customers. We operate like a startup within AWS, offering you the opportunity to tackle complex challenges while working with the latest technologies in deep learning, large language models, and optimization. If you are passionate about pushing the boundaries of applied science, thrive in ambiguous problem spaces, and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources, we want to hear from you. Key job responsibilities - Design, develop, and deploy machine learning models for demand forecasting, inventory optimization, anomaly detection, and supply chain decision-making. - Develop generative AI and agentic AI solutions that automate complex supply chain workflows and deliver intelligent, adaptive recommendations to customers. - Formulate real-world business problems as machine learning problems; define data requirements, model architectures, evaluation metrics, and experimentation frameworks. - Drive end-to-end applied science projects from ideation through experimentation, offline evaluation, A/B testing, and production deployment at scale. - Collaborate with engineering, product management, and business stakeholders to translate scientific capabilities into customer-facing product features, and mentor other scientists to raise the team's technical bar. A day in the life You will start many mornings reviewing experiment results and model metrics before joining a science sync where you and your teammates discuss progress, debug tricky modeling issues, and brainstorm new approaches. From there, you may spend focused time writing and testing model code in Python or PyTorch, running offline evaluations, or preparing an A/B test for a new forecasting algorithm. Expect regular working sessions with software engineers to integrate your models into production services, and occasional deep-dive reviews where you present your scientific approach and findings to the broader team. About the team The AWS Applied AI Solutions team builds enterprise applications that leverage Amazon's operational expertise to solve real-world supply chain challenges for millions of companies. We operate like a startup within AWS, moving fast and shipping iteratively with modern AI technologies. We invest in your growth through mentorship from experienced scientists, conference publication support, and internal science reading groups. If your career hasn't followed a traditional path, we encourage you to apply — we value varied experiences and perspectives.
Manager, Applied Science- Manipulation & Autonomy , Fauna
US, NY, New York
We are seeking a Lead Applied Scientist to drive the development of next-generation manipulation and autonomy systems for robots operating in complex, real-world environments. This role is designed for an exceptional technical leader with a strong research background and a passion for translating cutting-edge ideas into working robotic systems. You will play a central role in defining the technical direction of our manipulation and autonomy stack, from problem formulation and algorithm design to system integration and real-world deployment. While this role is hands-on and deeply technical, it is also expected to evolve toward technical leadership and team building over time. Key job responsibilities - Lead research and development of manipulation and autonomy systems for robots, including planning, control, learning, and closed-loop execution - Design and implement algorithms, especially focused around autonomy and interaction with dynamic environments - Work closely with perception, motor control, hardware, and systems teams to build tightly integrated autonomy pipelines - Define evaluation methodologies, benchmarks, and simulation-to-real workflows for manipulation and autonomy - Stay deeply engaged with the research community, selectively incorporating state-of-the-art techniques into deployed systems - Mentor and guide other researchers and engineers, help set technical direction, and contribute to team growth and research culture - Lead technical projects from conception through production deployment - Bridge research initiatives with practical engineering implementation 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.
Applied Scientist, AWS Applied AI Solutions - Life Sciences
US, WA, Seattle
As part of the AWS Applied AI Solutions organization, we have a vision to provide end user applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are easy to adopt and easy to use. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions, where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients, powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models, large language models, and agentic reasoning systems to life sciences problems, then put them into the hands of customers as applications and managed services they can fine-tune, tailor, and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological, regulatory, and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In clinical trials, we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor, encourage publication, and support conference participation. If you want to do research that ships, this is the team. The Role We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products, with a primary focus on clinical trial operations and agentic reasoning. You will design, train, and evaluate systems that reason over complex clinical and operational logic, and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems, own your results end to end, and see your work reach production. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason, plan, and act in complex scientific domains, while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: - How do you build LLM-based agentic systems that correctly reason over clinical protocols, regulatory standards, and complex multi-step operational workflows? - How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? - How do you develop model customization methods (fine-tuning, retrieval augmentation, domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation, structured reasoning, evaluation, domain adaptation), with opportunities to contribute across drug discovery (protein engineering, antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that scientists use daily. Key job responsibilities - Design, train, fine-tune, and evaluate LLM-based agentic systems that reason over clinical protocols, regulatory standards, and operational workflows - Build rigorous evaluation harnesses and benchmarks to measure agent reliability, faithfulness, and failure modes in high-stakes domains - Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that help customers get better outputs on their own data with less effort - Contribute to graph-based and causal modeling approaches for clinical trial operations - Partner with Life Sciences domain experts, product, and engineering to translate scientific challenges into shipped capabilities - Own experiments end to end: problem framing, implementation, evaluation, iteration, and hand-off to production - Publish at top-tier venues where the work supports it - Contribute to drug discovery efforts (protein engineering, antibody design) as opportunities arise A day in the life - Design and run an experiment to validate a new agentic reasoning or fine-tuning method, then ship it as a capability customers can use - Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone - Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic - Meet with domain experts to scope what the next model release needs to do - Review results with a senior scientist, sharpen the approach, and get it over the finish line - Prototype a new idea that could become the next capability in the product About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & 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, mentorship 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
Senior Applied Scientist, Center for Quantum Computing: Device Team
US, CA, Pasadena
As a Senior Quantum Applied Scientist on our Device team, you will be a technical authority and driving force in the design and measurements of novel superconducting qubits. You will lead detailed simulation and measurement efforts to explain experimental results, inform new qubit designs, and optimize device performance, working collaboratively with our design, fabrication, processor, and exploratory research teams. This is a role with significant room for innovation: you will play a key role in finding paths towards more performant devices. We are looking for a seasoned researcher with deep expertise in superconducting circuit physics plus a proven track record of bridging design, simulation, and measurement. Success in this role requires both technical depth and a genuine passion for applied, collaborative work. The ideal candidate will excel at communication across disciplines — translating detailed analyses into actionable guidance for engineering teams — and will bring the experience and judgment to identify innovations that will have the greatest impact. Key job responsibilities • Develop simulations to predict device performance, then design and measure devices that leverage the understood scalings. • Reduce the gap between simulated predictions and measurements by building more accurate models. • Communicate scientific findings across the CQC, and, when appropriate, share results externally via conference presentations and publications in scientific journals • Identify and evaluate emerging research developments that could impact design decisions About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer.
Sr. Data Scientist, Amazon Pay Data Products
IN, KA, Bengaluru
Amazon Pay strives to be Earth’s most customer-centric payments service. Our mission is to serve customers and merchant partners with the most trusted, friction-less and rewarding payment solutions for their needs on and off Amazon. We are seeking an exceptional Data Scientist III to drive innovation in machine learning and artificial intelligence solutions while leading high-impact initiatives across the organization. Key job responsibilities Technical Excellence Lead end-to-end machine learning projects using PyTorch, AWS SageMaker, and other leading ML frameworks Design and implement complex statistical models and deep learning solutions Develop and optimize MLOps pipelines for model training, evaluation, and deployment Experience with modern LLM frameworks and Generative AI applications Expertise in Python, R, and related data science libraries MLOps & Development Build automated ML pipelines using AWS services (CodePipeline, Lambda, Step Functions) Implement CI/CD practices for ML model deployment and monitoring Create containerized solutions using Docker for scalable model deployment Experience with model optimization and hyperparameter tuning using tools like Optuna Integrate ML solutions with monitoring tools like MLflow Business Impact & Leadership Partner with stakeholders to translate business problems into technical solutions Design and develop business intelligence applications for real-time insights Lead technical initiatives and mentor junior data scientists Drive cross-functional collaboration to deliver innovative solutions Communicate complex technical concepts to non-technical audiences About the team The Amazon Pay Data Products team is a central unit that builds and maintains data products supporting Amazon Pay's growth across multiple markets. We operate at scale, processing 150M+ monthly transactions and managing 12 PB of data infrastructure. Our team consists of Business Intelligence Engineers, Data Engineers, and Product Managers who develop and maintain standardized reporting, data marts, and self-service analytics tools. Our expanded capabilities cover data science and Gen AI wherein we have built our first suite of multi-agent systems.
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