UX Designer I , Talent Acquisition - Products
Amazon · Seattle, Washington, USA ·
- Seniority
- Junior
- Category
- Design
Hiring is one of the most consequential decisions a person makes or receives, and the tools that power it are becoming genuinely agentic. We are seeking a UX Designer I to design the experiences behind our high-volume hiring portfolio at this inflection point. Amazon's Talent Solutions Product Design & Research team is looking for a UX Designer I who brings strong craft, curiosity, and an aptitude for team collaboration. You will work across our associate hiring domain, delivering design solutions for hiring workflows for Amazon's fulfillment centers, customer service, and grocery operations. You will partner with product management, engineering, and research to deliver human-centered experiences that simplify hiring at scale.
Amazon hires at a scale few companies ever approach: hundreds of thousands of roles a year across fulfillment, corporate, and technical positions worldwide, with application volumes that can reach millions in a single week. The products you will help shape increasingly act on behalf of the people who power that volume: defining recruiter tasks, supporting candidate inquiries, managing hiring appointments and shift changes. Your challenge is translating systems into interactions that feel trustworthy, purposeful, and in control at high-volume scale. Where a recruiter managing hundreds of open roles can understand what the system did, why, and what happens next, and where a candidate applying to an entry-level warehouse role receives the same quality of experience as one interviewing for a senior engineering position.
Key job responsibilities
▪ Deliver design solutions for defined hiring workflows, from research synthesis through interactive prototypes, presenting a variety of solutions and clearly representing the benefits and trade-offs of each approach.
▪ Partner with engineering and product management in the execution and delivery of successful products and services, using a customer-focused, iterative design process. Work closely with engineering to effectively incorporate the capabilities and constraints of the technology stack into your solutions.
▪ Design for AI-assisted high-volume hiring surfaces where the stakes of a bad pattern are amplified at scale. In HVH, AI systems screen candidates, manage appointment scheduling, surface availability windows, and flag qualification mismatches, often acting thousands of times a day across millions of applicants. Your job is to make those actions legible: when the system qualifies or disqualifies someone, the candidate needs to understand what happened and what comes next. When a recruiter sees an AI-generated recommendation or risk flag, it needs to be honest about confidence and give them a clear path to act or override. You contribute to the trust layer between a probabilistic system and a person who may be applying to their first job or managing a warehouse of open roles. That means edge cases, low-confidence states, and failure modes are first-class design problems, not afterthoughts.
▪ Determine best UX solutions based on customer feedback and business goals. Synthesize qualitative and quantitative data to inform design decisions at the feature level.
▪ Communicate design rationale, outcomes, and deliverables clearly to partners and stakeholders. Present and critique work in cross-team reviews to improve your own work.
▪ Apply and extend the use of established UX techniques, templates, and assets. Reuse UX artifacts and keep consistency across your projects.
A day in the life
You start the morning in a design critique. You have been working on the appointment confirmation flow for a candidate applying to a fulfillment center role, and you come with three directions and a clear point of view on which one is right. The feedback is direct. One direction gets cut immediately. Another gets pushed on details that matter: what does the candidate see if the system cannot confirm a shift? What does the error state communicate? You leave with sharper answers than you arrived with and a revised prototype due by end of week. That is a normal Tuesday.
By mid-morning you are pairing with your engineering partner on the spec for an AI-driven scheduling recommendation feature. The model surfaces a suggested appointment window with a confidence signal, and you are working through what happens when availability data is stale or incomplete. You care about that case because your candidate is a first-time applicant who may not know to try again. Together you land on an interaction that is honest about what the system knows and gives the candidate a clear next step without dead-ending them.
In the afternoon you notice that the team's shared component for displaying AI-generated status messages does not account for a partial-match state you just hit in your own flow. You flag it, draft a quick proposal with two options, and share it with the team. It is a small contribution but the kind that compounds: patterns that cover edge cases make everyone's work more consistent.
You end the day reviewing research synthesis your research partner shared on candidate drop-off during the scheduling step. Two findings directly affect your current design. You update your flows, note a question for the next research sync, and close your laptop knowing the work tomorrow will be better because of what you read today. The bar on this team is high and it is visible. You are here because you want to meet it.
About the team
Talent Solutions Product Design & Research designs the products that power how Amazon hires: the tools recruiters, hiring managers, interviewers, and candidates use across the hiring journey. We own the end-to-end experience across the recruiter, manager, and candidate experiences, and increasingly put AI to work in service of faster, fairer, more human hiring. We are designers, researchers, and writers who work backward from the people we serve, enable teams across the organization with scalable design patterns and systems, and raise the bar on quality at scale.
Amazon hires at a scale few companies ever approach: hundreds of thousands of roles a year across fulfillment, corporate, and technical positions worldwide, with application volumes that can reach millions in a single week. The products you will help shape increasingly act on behalf of the people who power that volume: defining recruiter tasks, supporting candidate inquiries, managing hiring appointments and shift changes. Your challenge is translating systems into interactions that feel trustworthy, purposeful, and in control at high-volume scale. Where a recruiter managing hundreds of open roles can understand what the system did, why, and what happens next, and where a candidate applying to an entry-level warehouse role receives the same quality of experience as one interviewing for a senior engineering position.
Key job responsibilities
▪ Deliver design solutions for defined hiring workflows, from research synthesis through interactive prototypes, presenting a variety of solutions and clearly representing the benefits and trade-offs of each approach.
▪ Partner with engineering and product management in the execution and delivery of successful products and services, using a customer-focused, iterative design process. Work closely with engineering to effectively incorporate the capabilities and constraints of the technology stack into your solutions.
▪ Design for AI-assisted high-volume hiring surfaces where the stakes of a bad pattern are amplified at scale. In HVH, AI systems screen candidates, manage appointment scheduling, surface availability windows, and flag qualification mismatches, often acting thousands of times a day across millions of applicants. Your job is to make those actions legible: when the system qualifies or disqualifies someone, the candidate needs to understand what happened and what comes next. When a recruiter sees an AI-generated recommendation or risk flag, it needs to be honest about confidence and give them a clear path to act or override. You contribute to the trust layer between a probabilistic system and a person who may be applying to their first job or managing a warehouse of open roles. That means edge cases, low-confidence states, and failure modes are first-class design problems, not afterthoughts.
▪ Determine best UX solutions based on customer feedback and business goals. Synthesize qualitative and quantitative data to inform design decisions at the feature level.
▪ Communicate design rationale, outcomes, and deliverables clearly to partners and stakeholders. Present and critique work in cross-team reviews to improve your own work.
▪ Apply and extend the use of established UX techniques, templates, and assets. Reuse UX artifacts and keep consistency across your projects.
A day in the life
You start the morning in a design critique. You have been working on the appointment confirmation flow for a candidate applying to a fulfillment center role, and you come with three directions and a clear point of view on which one is right. The feedback is direct. One direction gets cut immediately. Another gets pushed on details that matter: what does the candidate see if the system cannot confirm a shift? What does the error state communicate? You leave with sharper answers than you arrived with and a revised prototype due by end of week. That is a normal Tuesday.
By mid-morning you are pairing with your engineering partner on the spec for an AI-driven scheduling recommendation feature. The model surfaces a suggested appointment window with a confidence signal, and you are working through what happens when availability data is stale or incomplete. You care about that case because your candidate is a first-time applicant who may not know to try again. Together you land on an interaction that is honest about what the system knows and gives the candidate a clear next step without dead-ending them.
In the afternoon you notice that the team's shared component for displaying AI-generated status messages does not account for a partial-match state you just hit in your own flow. You flag it, draft a quick proposal with two options, and share it with the team. It is a small contribution but the kind that compounds: patterns that cover edge cases make everyone's work more consistent.
You end the day reviewing research synthesis your research partner shared on candidate drop-off during the scheduling step. Two findings directly affect your current design. You update your flows, note a question for the next research sync, and close your laptop knowing the work tomorrow will be better because of what you read today. The bar on this team is high and it is visible. You are here because you want to meet it.
About the team
Talent Solutions Product Design & Research designs the products that power how Amazon hires: the tools recruiters, hiring managers, interviewers, and candidates use across the hiring journey. We own the end-to-end experience across the recruiter, manager, and candidate experiences, and increasingly put AI to work in service of faster, fairer, more human hiring. We are designers, researchers, and writers who work backward from the people we serve, enable teams across the organization with scalable design patterns and systems, and raise the bar on quality at scale.