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Amazon Nova AI Challenge: Trusted Software Agents, Rules

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The Amazon Nova AI Challenge: Trusted Software Agents (the “Competition”) is a university competition sponsored by Amazon.com Services LLC (“Sponsor”), in which 10 teams of college or university students compete to accelerate advances in artificial intelligence (“AI”). In this year’s Competition track student teams will focus on secure multi-turn development of web applications using coding assistants. Each team will take on the role of either a ‘model developer’ or ‘red team’. The model developer teams will focus on building LLM-driven coding assistants that can complete complex software engineering tasks to modify and extend full stack web applications. In addition to adding capabilities, coding assistants built by model developer teams must prevent accidental or purposeful introduction of security vulnerabilities into the application. To this end, model developer teams will also build bots that simulate malicious users. They will use these malicious user bots to test their systems and then use these same bots to test the resilience of opposing model developer teams during the Competition. 

Red teams, in contrast, will develop LLM-enabled bots capable of identifying and exploiting vulnerabilities found in the web applications generated by the model developer teams. In addition, red teams will build bots that simulate benign users that communicate with the coding assistants to complete software engineering tasks. 

See below for the Competition details.

By applying to or participating in the Competition, you agree to these Official Rules. Please read them carefully and completely.

COMPETITION CALENDAR

Applications to participate in the Competition can be submitted starting November 10, 2025,  through 11.59 PM PST December 7, 2025. The Competition will run from February  2026 through September 2026 (the "Competition Period"). The Competition phases are noted in the calendar below. The dates are approximate and are subject to change at the Sponsor’s sole discretion.

Phases

Starts on

Phase 1: Participant Application Period

11/10/2025

Phase 2: Sponsor Application Review Period

12/8/2025

Phase 3: Participant Notification Period and Onboarding

12/22/2025

Bootcamp

2/1/2026

Phase 4: Initial Build Period

2/4/2026

Phase 5: Tournament Period

March 2026

Phase 6: Finals Event

September 2026

Winners Announced

October 2026

COMPETITION OVERVIEW

Participating University or college teams will focus on advancing the capabilities of coding assistants, while improving their robustness with respect to security principles. Participating teams will aim to make coding assistants for full stack web development that are maximally capable and preclude vulnerabilities and cyber security threats. During the Competition, each team will focus either on building coding assistants (i.e. model developer teams) or developing automated red teaming techniques to identify potential exploits (i.e. red teams). In addition to building their coding assistant, each model developer team will also be responsible for developing a simulated malicious user that they can use for testing their coding assistant and then in tournaments use them to test the resilience of coding assistants built by other teams. In addition to building their red teaming system, each red team will also be responsible for developing a simulated benign user that will interactively prompt each competing coding assistant to complete a specified task. In applying to the competition, each team may choose to apply for either the red team role or the model developer role, or they may choose to apply for both roles and be assigned a role at the Sponsor’s discretion. Throughout the competition, red teams and model developer teams will participate in a series of tournaments. The tournaments will have two phases, in the first ‘build’ phase each of the model developer teams will be matched up with simulated benign and malicious users in multi-turn conversation directed toward completion of several specified tasks on one of more software repos. This phase will be evaluated through automatic testing of the functionality required to be added to the application in each task. In the second, ‘validation’ phase, each of the generated repos will be tested by the automated red teaming agents, which will seek out and exercise vulnerabilities in the applications. 

ELIGIBILITY

To be eligible to participate in the Competition, you must: (1) be enrolled as a full-time undergraduate or post-graduate student at an accredited college or university (other than colleges or universities located in any of the Restricted Jurisdictions defined below) (“Universities”) and remain a full-time student in good standing at such college or university while participating in the Competition; (2) be at or above the age of majority in your country, state, province or jurisdiction of residence at the time of entry into the Competition; and (3) not be a person or entity subject to U.S. export controls or sanctions, including citizens of any of the Restricted Jurisdictions . Competition is void in Cuba, Iran, Syria, North Korea, Sudan, the region of Crimea, the so-called Donetsk People’s Republic (DNR) or Luhansk People’s Republic (LNR), and where prohibited by law (each, a “Restricted Jurisdiction”). People who, during the Competition Period, are directors, officers, employees, interns, and contractors (“Personnel”) of Sponsor, its parents, subsidiaries, affiliates, and their respective advertising, promotion and public relations agencies, representatives, and agents (collectively, “Competition Entities”), immediate families members of such Personnel (parents, siblings, children, spouses, and life partners of each) and members of the households of such Personnel (whether related or not) are ineligible to participate in this Competition. Sponsor reserves the right to verify eligibility and to adjudicate any dispute at any time.
Entrants (“Entrants”) must enter as part of an “Entrant Team” consisting of one or more students from a single college or university. An Entrant is only permitted to be part of one Entrant Team. Any Entrant that is part of more than one Entrant Team may be disqualified, and the corresponding Entrant Teams may be disqualified at the sole discretion of Sponsor. Only one member of the Entrant Team may submit an application on behalf of an Entrant Team, but all those listed on the application are Entrants. All prize money payable to Entrant Teams will be split evenly among eligible Entrants of a winning Entrant Team (based on the pre-tax amount of prize money), as identified in the application, as updated as permitted by these Official Rules, and who maintain full-time student status in good standing at their college or university during the entire Competition Period.
Each team must select a faculty advisor, who, with respect to that team, will act as the official representative for the Entrant Team’s university or college (the “Faculty Advisor”). Each Entrant Team must have its own Faculty Advisor; Faculty Advisors may not represent multiple teams. Faculty Advisors are not members of the Entrant Team and will not receive any portion of any prize. Faculty Advisors must remain full-time employees of the Entrant Team university or college during the entire Competition Period. During the Competition Period, but no later than July 15, 2026, the Faculty Advisor may request to remove members from the Entrant Team or to add additional members to the Entrant Team. The Faculty Advisor must provide an explanation of the reason for the removal or addition, and any proposed new member must provide documentation requested by Sponsor and agree to comply with these Official Rules. Changes to the Entrant Team are subject to Sponsor’s approval in its sole discretion. If at any point during the Competition Period, the Entrant Team’s Faculty Advisor cannot continue to serve as a Faculty Advisor, the Entrant Team may submit a request to Sponsor to select a new Faculty Advisor. Changes to the Entrant Team’s Faculty Advisor are subject to Sponsor’s approval in its sole discretion. If an Entrant Team fails to have a Faculty Advisor at any point in time during the Competition, for any reason, the Entrant Team may be disqualified.
Each Entrant must be eligible to participate in this Competition and comply with these Official Rules or the Entrant, and the Entrant Team associated with that Entrant, may be disqualified. This Competition is subject to all applicable federal, state, territorial, provincial, and local laws. Competition is void where prohibited. By participating in the Competition, all Entrants accept and agree to comply with and abide by these Official Rules and the decisions of the Sponsor which will be final and binding, including the Sponsor’s right to verify eligibility, to interpret these Official Rules, and to resolve any disputes relating to this Competition at any time. Only Entrants may work on the Competition or their entries (e.g., developer systems, coding assistants, red-teaming systems, or user simulators), although employees of the Sponsor may provide support to the Entrant Team during the Competition Period, and the Faculty Advisor, and other students and faculty members at an Entrant Team’s university or college may provide support and advice to the Entrant Team and may co-author the Technical Article (as defined below) or other research papers.

ADDITIONAL REQUIREMENTS

During all phases of the Competition, Sponsor may in its sole discretion require Entrant Teams to provide periodic status updates, reports, or demonstrations of the developer systems, coding assistants, red-teaming system or user simulators. Sponsor may also require Entrant Teams to comply with additional rules, requirements, or procedures that Sponsor determines, in its sole discretion, are necessary for the administration of the Competition. Sponsor may in its sole discretion penalize teams for noncompliance with any rules, requirements, or procedures, including disqualification from the Competition.

Sponsor may provide Entrants selected to participate in the Competition access to generative AI models, software, software platform, software development kits, libraries, APIs, documentation, sample code, data sets, and related materials (“Program Materials ” and “Restricted Program Materials”) that may be used in connection with the Competition. If an Entrant uses any Program Materials or Restricted Program Materials, the Entrant is subject to and agrees to comply with Sponsor’s Program Materials License Agreement or a substantially similar alternative license that may be applied at Sponsor’s sole discretion. Program Materials and Restricted Program Materials may include APIs, data sets, models, model outputs, and other materials that are not public (“Non-Public Materials”).
Each Entrant and Faculty Advisor agrees that they will not disclose, distribute, or otherwise make available any Non-Public Materials to anyone other than other Entrants of their Entrant Team and the Entrant Team’s Faculty Advisor. Each Entrant (and each Faculty Advisor) agrees that they will use Non-Public Materials only in connection with the development of a developer systems, coding assistant, red-teaming system, or user simulator as part of the Competition and in compliance with these Official Rules. Entrants (and Faculty Advisors) may not use Non-Public Materials for any other purpose. Entrants (and Faculty Advisors) must return or destroy all Non-Public Materials (in any form and including, without limitation, all summaries, copies and excerpts of the same) promptly following any request from Sponsor, or if the Entrant Team discontinues the development or operation of its model or red-teaming system. If any Entrant or Faculty Advisor is disqualified from the Competition, leaves the university, or college or otherwise terminates their participation in the Competition, that individual must immediately return or destroy all Non-Public Materials in their possession. Sponsor reserves the right in its sole discretion to impose additional terms and conditions on the use of Program Materials, and to condition access to Program Materials on Entrants’ agreement to those terms and conditions.

Each Entrant Team and Faculty Advisor agrees that they will not copy, disclose, distribute, or otherwise use any output created by a generative AI model during this competition for any purpose other than the development or improvement of their systems as part of this Competition. Entrant Teams may not publish any model output or publish or deploy their systems for any purpose, including in connection with any paper submission, without prior review and approval by Sponsor.

DESCRIPTION OF COMPETITION PHASES

PHASE 1 “Participant Application Period”: Between November  10, 2025 and December 7, 2025, the student leader of each Entrant Team that wishes to enter the Competition may visit  amazon.science/nova-ai-challenge (the “Competition Site”) to submit their entry information via the YouNoodle, Inc. (“YouNoodle”) application portal, including but not limited to: complete names, contact information, and resumes of all Entrant Team members, proof of university or college enrolled status (e.g., verification of enrollment or an uploaded copy of Entrants’ student IDs), name and contact information of up to three sponsoring Faculty Advisor(s) from the Entrant Team’s university or college , and a bio for each Entrant Team member and Faculty advisor(s).

Sponsor will consider applications by multiple different teams from a single institution, but each Entrant Team must have their own Faculty Advisor, and there can be no overlap in the composition of the Entrant Teams or Faculty Advisors. If more than one Entrant Team is accepted from any university or college, the Entrant Teams will be expected to work independently, and any collaboration or coordination among Entrant Teams will be grounds for disqualification.

Entry Applications may be submitted at any time during the Participant Application Period. Only one individual per Entrant Team, preferably the team lead, may submit an Entry Application on behalf of their Entrant Team. Other members of the Entrant Team must accept the invitation to join the team via the YouNoodle application portal. All Entrants must create an account with YouNoodle if they have not done so already to submit an Entry Application or accept an invitation to join an Entrant Team and participate in the Competition. Creating and maintaining a YouNoodle account is free of charge. All Entry Applications must be complete when the Participant Application Period closes at 11:59 pm Pacific Time on the last day of the Participant Application Period. Entry Applications are not complete until all the online prompts and instructions to upload the Entry Application have been properly followed, the Official Rules have been accepted, and all Entrant Team members and the Faculty Advisors have accepted their invitations to join the Entrant Team via the YouNoodle application portal. Entry Applications may not be revised once submitted. Once submitted, Entry Applications will not be returned and become the property of the Sponsor.

Entry terms: Determination of eligibility and compliance with these Official Rules and any other requirements imposed by Sponsor will be in the sole discretion of the Sponsor. By entering, Entrants represent that all information and materials submitted to Sponsor in connection with the Competition:

  1. are the original work of the Entrant Team or an update to an original work of the Entrant Team;
  2. do not infringe or violate the rights of any third party, including but not limited to copyrights, trademarks or copyrighted material not owned by the Entrant Team, contract and licensing rights, rights of publicity or privacy, moral rights, or any other intellectual property rights; and
  3. are not subject to any third-party agreements, and that Sponsor will not be required to pay or incur any sums to any person or entity as a result of its exercise of any rights granted under these Official Rules.

As referenced above, each Entrant Team will be required to apply for at least one role (i.e., either the red-team role or model developer role). Alternatively, Entrant Teams can choose to apply for both roles and Sponsor will assign one of the two roles to a team based on the Entry Application in Sponsor’s sole discretion.

PHASE 2 “Sponsor Application Review Period”: All eligible Entry Applications will be reviewed by the Sponsor. Sponsor will select up to ten (10) Entrant Teams for the Competition in its sole discretion, based on the following criteria:

  • The technical merit of the approach;
  • The novelty of the idea; and
  • An assessment of the Entrant Team’s ability to execute their plan.

PHASE 3 “Participant Notification Period”: Entrant Teams selected by Sponsor to participate in the Competition will be notified by email at the email address provided in the application portal. Entrant Teams acknowledge that some Entrant Teams may include Entrants and Faculty Advisors who participated in prior competitions and other promotions offered by Sponsor.

Stipends: Each Entrant Team selected to participate in the Initial Build Period will be eligible to receive a restricted research grant of $250,000 U.S. dollars (paid in any number of installments as determined by Sponsor in its sole discretion, subject to Entrant Team’s continued participation in and eligibility for the Competition). These restricted research grants will be awarded to the universities and not the Entrant Teams or any individual Entrant, and will be subject to the university or college signing and returning any agreements or other documents required by Sponsor (including IRS forms W-9 and/or W-8), and to the university or college agreeing in writing that no more than 35% of the research grants may be allocated to administrative fees. Entrant Teams will not be eligible to begin participating in the Competition until all required agreements and other documentation have been completed by the respective university or college. The grants are intended to support two full-time students or the equivalent of two full-time students during the Competition and one month of the Faculty Advisor’s salary. Each university or college will be responsible for allocating and managing the funds within these guidelines and for payment and reporting of any required taxes, withholdings, fees, or duties. Sponsor is not responsible for managing the funds, including their allocation or distribution by the university or college, after they are paid to the university or college. Each member of an Entrant Team whose university or college receives a stipend award, and its Faculty Advisor, will also receive free AWS services to support the development of their model or red-teaming system (subject to reasonable limitations set by Sponsor), and support from the Sponsor as determined by Sponsor.

Stipends are non-transferable except as directed by Sponsor. No stipend substitutions or cash redemptions are allowed except as designated by Sponsor. Except where prohibited by law, all federal, state, provincial, or other tax liabilities or withholdings are the responsibility of the university or college and the Sponsor will not be responsible for any tax deductions which may be necessary, except that Sponsor may withhold taxes as required by law. Universities are responsible for any costs and expenses associated with stipend acceptance and use. If an Entrant Team withdraws from the Competition or does not remain compliant with these Official Rules, Sponsor will be relieved of any obligation to pay any remaining portion of the stipend to the Entrant Team’s university or college All details relating to the stipend not specified herein shall be determined solely by Sponsor.

Boot Camp: Selected Entrant Teams will be invited to a Competition Boot Camp (“Boot Camp”), which Sponsor intends to hold in February 2026 at Sponsor’s headquarters in Seattle, Washington or alternative location as determined by Sponsor. Entrant Teams will be expected to use the stipend towards any uncovered costs associated with the Boot Camp. Faculty Advisors and at least 1 student team member are expected to attend the Boot Camp. In the event that Sponsor is unable to hold a Boot Camp, Sponsor may reschedule or cancel the Boot Camp at its sole discretion, and Sponsor is not required to reimburse any expenses incurred by Entrant Teams.

PHASE 4 “Initial Build Period”: Selected Entrant Teams will develop their model or red-teaming system. Sponsor will provide each Entrant Team with certain Program Materials or Restricted Program Materials by the beginning of the Initial Build Period.

PHASE 5 “Tournament Period”: The Tournament Period will include a series of tournaments. The number of individual tournament rounds, and the exact start and end dates of each of the tournaments will be announced prior to the start of the first tournament. On the start date of each of the tournaments, red teams will submit their red-teaming systems and benign user simulator to the sponsor. On the start date of each of the tournaments. model developer teams will submit their coding assistant and malicious user simulator to the Sponsor. The Sponsor will then execute the build phase of the tournament, matching the different benign user simulators and malicious user simulators with each of the relevant coding assistants. Utility performance will be determined through automated tests associated with each development task. Following the build phase, in the validation phase, the Sponsor will match up each red teaming system with each relevant created applications and use a combination of automated testing and human evaluation to score the identified vulnerabilities and exploits in the applications. Red teams and model developer teams will be ranked separately. Overall ranking of model developer teams will be based on a combination of utility performance on the specified task and defense success in being resilient to exploits attempted by the red teaming bots. Overall ranking of red teams will be based on a combination of utility performance on the specified tasks and attack success in identification and execution of exploits by the red teaming bots.
 Evaluation results may be provided to the Entrant Teams, as determined by Sponsor in its sole discretion. Both red teams and model developer teams may improve their systems in between tournament rounds, after the Sponsor notifies them that a tournament round is complete. Teams may not modify their model or red-teaming systems while the tournament round is ongoing. To move to the Finals Event, teams must meet minimum success criteria that will be established and provided to Entrant Teams during the Tournament Period. Teams that do not meet the criteria at the end of the last tournament may be eliminated following the end of the current Tournament Period. 

Summit: Sponsor at its sole discretion may choose to hold a summit at the end of the Competition where participants discuss their findings and announce the winners of the Competition. Sponsor may elect to provide each finalist Entrant Team reimbursement for the cost of airline tickets (non-refundable coach class booked through Sponsor at least 14 days in advance) to the site of the Summit where Entrant teams will present their innovations from the competition, and the provision of hotel rooms at the site of the Summit or such other event, to permit members of each Entrant Team (up to a maximum number of members determined by Sponsor) and the Faculty Advisor to attend the Summit or such other event. Travel expense subsidies and access to the event may be subject to tax information reporting and withholding to the extent required by law.

Disclosure of Third-Party Funding. If any Entrant Team receives any third-party funding to facilitate its participation in this Competition, such funding must be disclosed to Sponsor no later than the last date of the Tournament Period, along with any requirements imposed on the Entrant Team in connection with the funding. Entrant Teams may not accept or use any third-party funding if acceptance or use of that funding, or any requirements imposed in connection with that funding, conflicts with these Official Rules.

Technical Publication: No later than the first day of the final tournament round in the Tournament period or any other date determined by the Sponsor, all participating Entrant Teams must submit to Sponsor a technical article including (a) the technical approach for their developer systems, coding assistant, red-teaming system, and user simulator, and (b) any comparative experiments performed by the Entrant Team and results of those experiments (a “Technical Article”). Entrant Team’s Technical Article must include sufficient detail to permit other researchers to replicate the work. However, Technical Articles may not include any Non-Public Materials or other confidential information of Sponsor or its affiliates. If a Technical Article does not provide sufficient detail to replicate the work, Sponsor may require the Entrant Team to provide Sponsor any additional information needed to replicate the work. Sponsor will publish the Technical Articles in connection with the Finals Event. Prior to the publication, Sponsor will not disclose Technical Articles to third parties. Entrant Teams may update their Technical Articles prior to the Finals Event.

In addition to the required Technical Articles, Entrants may publish other technical articles describing their work (“Additional Articles”). However, prior to submitting any technical articles to any publication, conference, or other venue for publication, Entrant Teams must obtain Sponsor’s written approval. Additional Articles may not include any Non-Public Materials or other confidential information of Sponsor or its affiliates. Entrants must submit any Additional Articles to Sponsor for review and comment at least two weeks prior to the submission deadline and must make, prior to submission, any changes or deletions requested by Sponsor to protect confidential or other sensitive information.

PHASE 6 “Finals Event”: Sponsor will hold a multi-day Finals Event that will be structured as follows. Sponsor will recruit a panel of coding and cybersecurity experts to serve as judges. These human judges will come onsite to the Sponsor’s campus (Entrant teams will not be onsite during the Finals Event) for the Finals Event. Expert human judges will evaluate both red teams and model developer teams’ success in finals. 

Human judges will work with model developer team’s coding assistants as both malicious and benign users and evaluate their effectiveness and ease of use. Human judges will also observe interaction between user simulator and coding assistants and inspect the results of automated security teaming bots. Human judges will conduct manual red teaming of generated applications. The final ranking and determination of winners for model developers will depend on 1) ease of use of coding assistant to complete tasks 2) quality of solution and 3) resistance of the resulting solution to attempted exploits. The final ranking and determination of winners for red teams will depend on 1) effectiveness in automated identification of exploits 2) quality of user simulation. 

Winners Announcement Event: Sponsor may elect to provide each finalist Entrant Team reimbursement for the cost of airline tickets (non-refundable coach class booked through Sponsor at least 14 days in advance) to the site of the event announcing the winners of the Competition, and the provision of hotel rooms at the site of the Event or such other event, to permit members of each finalist Entrant Team (up to a maximum number of members determined by Sponsor) and the Faculty Advisor to attend the Event or such other event. Travel expense subsidies and access to the event may be subject to tax information reporting and withholding to the extent required by law.

PHOTO, UNIVERSITY LOGO AND TEAM WRITE-UP REQUIREMENTS
Each selected Entrant Team must provide a team photo including all the team members and faculty advisor within 15 days of selection. No photo collage of individual pictures will be accepted. The team photos must be rectangular or square. Entrant Team photos that are strongly horizontal or vertical will not be accepted. The Entrant Team must provide a 50-word write-up about each individual team member and faculty advisor(s) within 15 days of selection. The write-up must be written in the third person. Every subsequent reference to the member must be a pronoun or last name. e.g.,: “Tom Smith is a computer science PhD student at ABC university, he/Smith is studying..” When requested by Sponsor, Entrant Teams must provide a high-quality university logo that the Sponsor is free to use for any marketing or promotional content for the Amazon Nova AI Challenge program.

PRIZES

Overall Performance Prize
Following the Finals Event, the two finalist red-teaming systems that attain the two highest ranks among the finalist red-teaming systems and the two finalist coding assistants that attains the two highest ranks among the finalist coding assistants will be the First-Place and Second-Place winners of the Overall Performance prizes. If there is a tie in the scores for any prize, Sponsor will rank the systems based on their Tournament Period performance. Sponsor’s decisions are final and binding in all matters relating to this Competition, including the determination of prize winners.

First-Place Overall Performance Model (1 winner): The Entrant Team that receives the highest rank for a coding assistant will receive $250,000 U.S. dollars awarded in the form of checks divided equally among all members of that Entrant Team.
Second-Place Overall Performance Model (1 winner): The Entrant Team that receives the second highest rank for a coding assistant will receive $100,000 U.S. dollars awarded in the form of checks divided equally among all members of that Entrant Team.
First-Place Overall Performance Red-Teaming System (1 winner): The Entrant Team that receives the highest rank for a red-teaming system will receive $250,000 U.S. dollars awarded in the form of checks divided equally among all members of that Entrant Team.
Second-Place Overall Performance Red-Teaming System (1 winner): The Entrant Team that receives the second highest rank for a red-teaming system will receive $100,000 U.S. dollars awarded in the form of checks divided equally among all members of that Entrant Team.

Prize Conditions: Prizes are non-transferable except as directed by Sponsor. No prize substitutions are allowed. Except where prohibited by law, all federal, state, provincial, or other tax liabilities are the responsibility of the prize winners, Sponsor will not be responsible for any tax deductions which may be necessary, and Sponsor reserves the right to withhold taxes as required by law. Prize winners will be responsible for paying all costs and expenses related to the prize that are not specifically mentioned, including, but not limited to, taxes, withholdings, and any other expenses that might reasonably be incurred by the winner in receiving or using the prize. All prizes awarded will be subject to any taxes Sponsor is required by law to withhold as well as applicable sales, use, gross receipts, goods and service, or similar transaction-based taxes. IF TAXES ARE APPLICABLE TO THE PRIZE(S), IT IS THE RESPONSIBILITY OF THE WINNER TO PAY TO THE APPROPRIATE AUTHORITIES. PAYMENTS TO COMPETITION WINNERS ARE SUBJECT TO THE EXPRESS REQUIREMENT THAT THE WINNER SUBMIT TO SPONSOR ALL DOCUMENTATION REQUESTED BY SPONSOR (INCLUDING FORMS W-9 OR W-8BEN AS REQUESTED BY SPONSOR) TO PERMIT COMPLIANCE WITH ALL APPLICABLE STATE, FEDERAL, LOCAL AND FOREIGN (INCLUDING PROVINCIAL) TAX REPORTING AND WITHHOLDING REQUIREMENTS. Prize winners are responsible for ensuring that the tax documentation submitted to Sponsor complies with all applicable tax laws and requirements. If a winner fails to provide the documentation or submits incomplete documentation, the prize may be forfeited and Sponsor may, in its sole discretion, select an alternate winner. Sponsor will divide all awards that are payable to any Entrant Team evenly among the Entrant Team members and distribute accordingly. Designation as a prize winner is subject to Entrant’s proof of compliance with these Official Rules, maintaining compliance with these Official Rules and approval by Sponsor. All details of prizes not specified herein shall be determined solely by Sponsor.

PRIVACY: Entrants and Faculty Advisors acknowledge and agree that Sponsor may collect, store, share, and otherwise use personally identifiable information provided during the application process and the Competition, including, but not limited to, name, mailing address, phone number, and email address. All personally identifiable information collected is subject to and will be used in accordance with Sponsor’s Privacy Notice ( www.amazon.com/privacy) and YouNoodle’s Privacy Policy ( www.younoodle.com/privacy), including for administering the Competition and verifying Entrants’ and Faculty Advisors’ identities, addresses, and telephone numbers in the event an entry qualifies for a prize. YouNoodle’s servers are located in the United States. By participating in this Competition, Entrants and Faculty Advisors authorize the transfer of personal data to the United States for purposes of administering the Competition, conducting publicity about the Competition, and additional purposes consistent with Sponsor’s goals or the Competition goals. By entering the Competition, Entrants and Faculty Advisors consent to Sponsor’s and YouNoodle’s collection, and Sponsor’s use and disclosure of entrants’ personally identifiable information for these purposes. The data controller for information collected by Sponsor is Amazon.com Services LLC, 410 Terry Ave North, Seattle, Washington 98109, USA.

INTELLECTUAL PROPERTY: By entering the Competition, each Entrant and Entrant Team represents and warrants that he or she has obtained all of the rights, licenses, and permissions in writing that are otherwise necessary for the Entrant Team to operate or distribute the model or red-teaming system and to grant to Sponsor the licenses set forth in these Official Rules and in the Developer Agreement. Entrants MAY NOT submit models or red-teaming systems created by any person other than themselves or their Entrant Team members.
As between Sponsor and Entrant Teams, models or red-teaming systems created by Entrant Teams will remain the property of the respective Entrant Teams or their university or college, excluding any Program Materials or Restricted Program Materials incorporated in the systems, which will remain the property of Sponsor. All output generated by any model or red-teaming system during the course of the Competition will be the property of Sponsor, and Entrant Teams will have limited rights to use the output for the sole purpose of improving or training their modes or systems for the purpose of this Competition. By submitting a model or red-teaming system in this Competition, each Entrant and Entrant Team represents and warrants that they own, or otherwise have the right to use and license, all of the intellectual property and other rights in and to the model or red-teaming system. Each Entrant and Entrant Team hereby grants Sponsor and its affiliates a non-exclusive, perpetual, irrevocable, worldwide, and royalty-free license to make, have made, use, sell, offer for sale, import, export, license, exploit, promote, reproduce, make available, publicly display, publicly perform, create derivative works of, and otherwise exercise all intellectual property and other rights in and to any concepts, works, inventions, information, designs, programs, software, or other materials that the Entrant or Entrant Team develops or submits in connection with the Competition or the creation of the model or red-teaming system, including any of the foregoing included or described in any Technical Article or other materials provided to Sponsor. In addition, upon Sponsor’s request, all Entrants and Entrant Teams must provide Sponsor all source code, datasets and algorithms developed in connection with the Competition. Each Entrant agrees to do or perform, or cause to be done and performed, all such further acts and things, and execute and deliver all such other agreements, certificates, instruments, and documents as Sponsor may reasonably request in order to carry out the intent and accomplish the purposes of the foregoing license.
Except where prohibited, each Entrant and Entrant Team further consents (and agrees to sign any additional documents required by Sponsor to formalize, effect, or perfect such consent) to Competition Entities’ model or red-teaming systems pursuant to these Official Rules and the use of any Entrant or Entrant Team names, likeness, biographical information, and voice in advertising, publicity, trade, and other marketing and promotional materials (including video, audio, and print through all means of distribution) worldwide without compensation, notice, or approval, and disclaims any ownership rights to the content of such materials.

Waiver, Release, and Limitation of Liability
EACH ENTRANT ACCEPTS THE CONDITIONS STATED IN THESE OFFICIAL RULES, AGREES TO BE BOUND BY THE DECISIONS OF SPONSOR, WARRANTS THAT THE ENTRANT IS ELIGIBLE TO PARTICIPATE IN THIS COMPETITION, AND AGREES TO RELEASE, INDEMNIFY, AND HOLD HARMLESS THE COMPETITION ENTITIES AND THE PERSONNEL OF EACH FROM AND AGAINST ANY AND ALL CLAIMS, LOSSES, LIABILITY, AND DAMAGES OF ANY KIND (INCLUDING REASONABLE ATTORNEYS’ FEES AND EXPENSES) ASSERTED AGAINST ANY OF THEM, INCURRED OR SUSTAINED IN CONNECTION WITH OR ARISING OUT OF ENTRANT’S PARTICIPATION IN THIS COMPETITION OR ANY TRAVEL OR OTHER ACTIVITY RELATED THERETO, USE OF ANY MODEL OR RED-TEAMING SYSTEM OR RIGHTS THEREIN, OR BREACH OF ANY AGREEMENT OR WARRANTY ASSOCIATED WITH THE COMPETITION, INCLUDING THESE OFFICIAL RULES. ANY ATTEMPT TO DELIBERATELY DAMAGE ANY WEBSITE OR UNDERMINE THE LEGITIMATE OPERATION OF THE COMPETITION MAY BE A VIOLATION OF CRIMINAL AND CIVIL LAWS AND, SHOULD SUCH AN ATTEMPT BE MADE, THE COMPETITION ENTITIES AND EACH OF THEIR LICENSEES RESERVE THE RIGHT TO SEEK ANY AND ALL REMEDIES AVAILABLE FROM ANY PERSONS RESPONSIBLE FOR ANY SUCH ATTEMPT TO THE FULLEST EXTENT PERMITTED BY LAW.

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Applied Scientist, Mobile Manipulation Robotics (I/O)
US, MA, North Reading
Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges. We're seeking a Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale. Key job responsibilities - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun!
Principal Applied Scientist, Items and Relationships Platform
US, WA, Seattle
Interested in setting the scientific vision for how every product at Amazon is understood, identified, and connected, with the opportunity to impact every Amazon customer and every selling partner? The Items and Relationships Platform is the foundation that establishes what every product in Amazon’s catalog is and how products relate to one another. We are rebuilding the platform to establish identity and relationships from meaning and context, using multimodal large language models and generative AI. This is a foundational problem: understanding products deeply enough to identify them, relate them, and keep the catalog trustworthy as it scales to the full breadth of Amazon’s global contribution traffic. A Principal Applied Scientist on the Items and Relationships Platform owns the science direction for product identity and product relationships. It spans how contributions are resolved into a committed product identity, how products are connected into structural relationships (variations, multipacks, newer versions, lines of products, bundles), how confident those decisions are, and how their quality is measured. You will own the strategy for multimodal understanding, representation learning, relationship inference, calibration, and evaluation, so that science capability compounds across identity and relationships rather than being built one use case at a time. This role lives at the intersection of research and production. Product identity and relationships are fundamentally an entity resolution problem: deciding what is the same product, and how different products relate, across diverse sources, languages, and formats. You will make the hard modeling calls on where sameness ends and relatedness begins, how models express and calibrate confidence, and how the platform measures quality when there is no external ground truth. Successful candidates are passionate about translating ambiguous business goals into a coherent science roadmap that enables many businesses at once. You are comfortable being the final scientific arbiter on hard cross-team decisions, and working alongside top-notch applied scientists and software engineers to turn advanced research into production systems. Key job responsibilities As a Principal Applied Scientist on the Items and Relationships Platform, you will: - Own the science direction for product identity: define how products are semantically understood, resolved, and committed as coherent identities, navigating the precision/recall and friction/selection trade-offs inherent in every decision - Build representations that decide both sameness and typed relatedness, and drive relationship inference from semantic identity rather than one pipeline per relationship type - Own structural relationship science across variations, multipacks and base products, newer versions, lines of products, and bundles, and discover new relationship types from the gap between defined policy and what identity reveals - Own calibration and evaluation science: define how models emit calibrated confidence, and build the LLM judge methodology and Catalog Identity Quality metric that measure identity and relationship quality at catalog scale - Partner with Principal Engineers on cost-aware modeling (cascades, distillation, routing) and close the loop from production signals and corrections back into training - Set science direction partnering with ACCS Applied Science teams, mentoring senior and principal-track scientists, and driving the documentation and publication of scientific innovation A day in the life On a typical day, you move between setting science direction and going deep on hard problems: reviewing a multimodal identity model or a relationship inference experiment with scientists, deciding how a borderline case (a multipack, a newer version, a near-identical variant) should be modeled, and pressure-testing whether a quality gain is real by examining the judge and its calibration. You also spend time zoomed out, shaping the longer-term science roadmap, aligning with Principal Engineers and partner teams on the identity and relationship signals they consume, and mentoring scientists as they take on the ambiguous, high-impact problems that define the platform’s future. About the team The Items and Relationships Platform advances state-of-the-art GenAI to deeply understand, uniquely identify, and meaningfully connect every product at Amazon scale. We build the canonical product identity, matching, and relationship systems that power Amazon’s universal catalog: eliminating duplicates, preventing abuse, keeping product families coherent, and integrating the world’s product data to power agentic shopping and trusted discovery for customers worldwide. We solve foundational challenges including schema-agnostic multimodal understanding, relationship inference, evaluation without ground truth, and efficient inference at reduced cost.
Senior Data Scientist, Amazon Leo
US, WA, Redmond
Amazon Leo is seeking a Senior Data Scientist to transform complex satellite, network, and customer terminal telemetry into actionable insights that drive system performance, reliability, and engineering decisions. In this role, you will work with large-scale datasets generated across satellite payloads, ground infrastructure, customer terminals, and end-to-end laboratory and on-orbit testing environments. You will develop analytical models, statistical methods, and automated data processing solutions to identify performance trends, detect anomalies, isolate issues, and support system validation and operational readiness. You will partner closely with systems, hardware, software, RF, and test engineering teams to establish meaningful performance metrics, correlate telemetry across multiple system components, and translate complex technical data into clear recommendations. Your work will directly influence engineering priorities, test strategies, system improvements, and launch readiness decisions. The ideal candidate combines strong statistical and data science expertise with practical experience analyzing complex engineering systems, processing high-volume time-series data, and developing scalable analytical solutions. Experience in satellite communications, wireless networks, or telecommunications is highly desirable. 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 - Develop and maintain scalable data processing and analytical solutions for satellite payload, ground network, customer terminal, and laboratory test telemetry. - Analyze large volumes of structured and unstructured data to identify system performance trends, anomalies, degradation, and potential failure conditions. - Correlate telemetry across satellite payloads, RF systems, networking infrastructure, customer terminals, and application-level measurements to identify relationships and isolate root causes. - Design statistical models, machine learning techniques, and predictive analytics to detect anomalies, characterize system behavior, and identify emerging reliability or performance issues. - Establish performance baselines, key performance indicators (KPIs), and statistical methodologies to evaluate system functionality, reliability, and performance across laboratory and on-orbit environments. - Analyze automated test results, test campaign data, and regression trends to identify software and hardware performance changes, recurring failures, and gaps in test coverage. - Develop dashboards, automated reports, and visualization tools that enable engineering teams and leadership to understand system health, performance, and readiness. - Apply statistical analysis and experimental design to compare software releases, hardware configurations, and system changes, quantifying their impact on end-to-end performance. - Translate complex telemetry and analytical findings into actionable engineering recommendations that influence test priorities, troubleshooting, product improvements, and operational decisions. - Collaborate across multiple engineering organizations to define data requirements, improve telemetry quality, and develop consistent analytical methodologies. - Lead the development of reusable analytical frameworks and scalable solutions that improve engineering efficiency, reduce manual investigation, and accelerate decision-making. - Mentor engineers and data scientists in statistical methods, telemetry analysis, data interpretation, and data-driven problem-solving.
Applied Scientist, PXT Central Science
US, WA, Seattle
The People eXperience Technology (PXT) Central Science (PXTCS)'s mission is to make PXT the most scientific, technologically proficient, and inclusive HR organization in the world. PXTCS does this by accelerating scientific rigor in business-led initiatives, working backwards from employee experience, and delivering science-driven products that improve the well-being of and value of work for Amazonian worldwide. We are seeking an Applied Scientist to build production machine learning systems that turn Amazon's employee listening signals into insight at scale. You will design, develop, and deploy ML solutions that transform large volumes of employee feedback — survey responses, free-text comments, and other listening channels — into signals that drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning natural language processing, large language models, predictive modeling, and generative AI applications applied to employee experience. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Apply and adapt state-of-the-art NLP and machine learning techniques to extract structured, reliable signals from employee listening data (surveys, free-text feedback, and related channels), using reasonable assumptions, data, and customer requirements. - Design, develop, and implement small-to-medium ML components on top of the employee listening system — such as theme and sentiment extraction, text classification, and aggregated signal generation — with input and guidance from senior scientists, taking ownership of the code in your components. - Write secure, stable, testable, maintainable, well-reviewed code (at the SDE I bar) to deliver solutions into production that benefit customers and the business. - Design features and signals responsibly, applying privacy-preserving practices appropriate to sensitive employee data. - Collaborate with cross-functional partners to understand business context and impact, and help mentor interns. - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership. About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We build and apply science on top of Amazon's employee listening systems — turning what Amazonians tell us into products and insights that improve their experience. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
Principal Applied Scientist, Frontier AI Assets , Frontier AI Assets
US, CA, Sunnyvale
The Frontier AI Assets (FAI) team is looking for a passionate, talented, and inventive Principal Applied Scientist with a strong deep learning and reinforcement learning background to build the training and evaluation assets that advance frontier Generative AI (GenAI) with Large Language Models (LLMs). AI models become more capable largely because of the learning environments, the RL gyms, in which they are trained. FAI builds those gyms and the platform that certifies them, so that model training teams across Amazon can rely on high-quality, graded assets. As a Principal on this team, you will set the technical direction for how these gyms and the tooling around them are built, evaluated, and scaled. Key job responsibilities As a Principal Applied Scientist with FAI, you will: •Set technical direction across data collection, sampling, and evaluation for RL gyms focused on agentic tool use across a range of real-world domains. •Design and lead experiments that produce reliable, efficient signal on what drives model improvement. •Build gym-building tooling and automated quality control that scale with demand. •Advance the science of how gyms are evaluated and validated before they're used in model training. •Act as a hands-on technical leader: produce exemplary, high-quality assets yourself, and audit and support more junior scientists and engineers. •Partner closely with domain experts and cross-functional teams, turning real-world problems into challenging, high-value tasks. •Lead scientific reviews, scrutinize experimental design and verification, probe assumptions, and align teams toward coherent strategies. Contribute to hiring, mentorship, and the development of other scientists. About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers. We build frontier training and evaluation assets, RL gyms and the certification platform behind them, that directly shape how Amazon's models are trained and improved. We value scientific depth, hands-on building, and turning research into measurable impact at scale.
Applied Scientist, Amazon Robotics
US, CA, Sunnyvale
Amazon Robotics is seeking an exceptional Applied Scientist to join our Foundation Models and Agentic AI team. This role presents an opportunity to shape the future of robotics through innovative applications of large vision-language-action (VLA) models, reasoning and task planning models, and reinforcement learning. At Amazon Robotics, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, depth, force, tactile, linguistic) - Accelerate policy skill acquisition through imitation learning and reinforcement learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities As an Applied Scientist in the Applied AI and Robotics Foundations Model team, you will: - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.
Senior Research Scientist, RBS Tech
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results Key job responsibilities As a Senior Research Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Senior Research Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
Sr Applied Scientist, Amazon Shipping
IN, HR, Gurugram
Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Sr Applied Scientist, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
Applied Scientist, North America Sort Centers, Amazon Transportation Services, North America Sort Centers, Amazon Transportation Services
US, WA, Bellevue
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, North America Sort Center Science team is for you . We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how we operate the middle mile network. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Key job responsibilities Key job responsibilities - Design and develop advanced mathematical optimization and machine learning solutions in the domains of network design and optimization. - Research, prototype, simulate, and experiment with these models using programming languages such as Java and Python; participate in the production level deployment. - Create, enhance, and maintain technical documentation and science designs. - Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders. - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.
Applied Scientist II, Prime Video - Ads Science
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities The science challenges include: - Heterogeneous customer responses. The same customer responds differently to ads depending on what they’re watching, how engaged they are, and their broader streaming context. We need to understand and predict this variation to make better advertising decisions in real time. - Signal sparsity in streaming. Unlike search or retail advertising, streaming offers no clicks, no conversions, and limited direct feedback. We must develop creative approaches to infer customer preferences, intent, and tolerance from indirect behavioral signals. - Personalization at scale. A one-size-fits-all ad experience leaves value on the table for both customers and the business. We build systems that adapt ad load, placement, and content to individual viewers across 100M+ customers and 100k+ titles. - Small effects, large variance. Ad interventions typically produce 0.1-2% shifts in engagement, effects easily overwhelmed by natural behavioral variance. Measuring, attributing, and optimizing these small signals requires rigorous experimental design and causal methodologies. - Competing objectives. Revenue, customer engagement, long-term retention, and advertiser value are in tension. We develop principled frameworks to navigate these tradeoffs and optimize for sustainable outcomes rather than any single metric. A day in the life - Contribute to the research and development of ML models that personalize advertising decisions for 100M+ customers across 100k+ titles, with production deployment in mind - Develop deep learning architectures (multi-task learning, embedding-based representations) for customer behavior prediction at scale - Design and analyze large-scale A/B experiments, applying causal inference techniques to measure and optimize the impact of ad strategies on customer engagement and monetization - Partner with engineering to ensure models meet production latency and scalability requirements - Collaborate with product managers to frame business problems as tractable ML problems and translate findings into product decisions - Shape the team’s scientific roadmap, identifying high-impact research directions About the team Prime Video Ads is pioneering the use of Generative AI, Deep Learning and Causal ML to empower the next generation of Ads experience in Prime Video. Our vision is to build the world’s most valuable and differentiated ad-supported streaming platform: one where every ad is relevant to the viewer, and every dollar spent by an advertiser delivers measurable results. We are seeking lead applied scientists who have demonstrated experience in advancing state of the art ML algorithms, particularly in Personalization, Relevance, Casual Inference and Gen-AI areas. Your role will be to deliver these innovations as production-ready systems at Amazon scale.
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