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 II 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 II who brings strong problem-solving skills, independent self-direction, and an aptitude for team collaboration. You will work across our associate hiring domain, delivering end-to-end 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.

Key job responsibilities

- Deliver end-to-end design solutions for difficult 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 design 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 and product level.
- Utilize strong storytelling and presentation skills to clearly communicate design strategy, outcomes, and deliverables to partners and stakeholders.



Present and critique work in cross-team reviews to improve your own work and share knowledge that helps improve the work of others.
- Improve design patterns, standards, methods, and/or processes for your team. Leverage, foster, and extend the use of established UX techniques, templates, and assets; propose new solutions or optimize existing ones, and foster UX artifact reuse and consistency across team members and 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 explicit 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, dra

📌 UX Designer II (Vancouver)
🏢 Amazon
📍 Vancouver