AMAZON · DESIGN MANAGEMENT CASE STUDY · 2025–2026
Building an AI-powered evaluation suite for Amazon’s interviewers.
Leading two designers across five AI-assisted tools that support interviewer judgment without replacing it.
Role · UX Design Manager · Team · 2 designers · Scope · AI workflows, interviewer platform, portfolio coherence
Context
Amazon’s hiring decisions depend on the judgment of more than 200,000 certified interviewers and roughly 10,000 Bar Raisers. Yet the surrounding process was heavy: people organized notes, prepared questions, compiled feedback, and reconstructed interview loops before debriefs. Bar Raisers alone spent an estimated 88,000 hours annually on administrative preparation. My team saw an opportunity to bring AI assistance into every stage of evaluation without displacing the human judgment that protects Amazon’s hiring bar.
The challenge
Each tool addressed a different failure point across interview preparation, capture, feedback, and debrief, and each had its own product and engineering constraints. UX served as the connective tissue, defining how the experiences could work as one coherent system. The persistent design question was where helpful AI becomes too directive. We needed to provide better questions, structure, and synthesis without suggesting ratings or recommendations that could undermine interviewer judgment.
My role
I managed the two designers responsible for hands-on feature design across the suite and the redesign of the platform that housed it. I partnered with product leadership on strategic direction, coached the team on where the true value was for interviewers, pushed back on technical constraints when necessary, and used critique to pressure-test the boundary between support and decision-making.
Approach
Rather than treating this as five feature launches, we organized the work around the arc of an interview loop. Designers set up structured reviews across PM and UX partners to surface conflicts early, and built holistic usability testing into project plans so in-flight features could be tested together. I stayed closely connected to every PM to anticipate changes, then coached the design team through the recurring question: does this AI interaction help an interviewer think more clearly, or does it start to tell them what to decide?
What we built
5 new features for AI-assisted evaluation workflows in Amazon's corporate recruiting tool:
Interview Prep Assistant · Generated tailored interview plans and competency questions, moving preparation from offline notes into a consistent, system-supported workflow.
Interview Transcription · Let interviewers stay present with candidates while preserving an accurate record for every downstream decision.
Interview Feedback Builder · Structured raw notes against Amazon’s writing guidelines while requiring interviewers to add their own evaluative judgment and final rating.
Debrief Prep Experience · Surfaced strengths, gaps, mixed signals, and follow-up questions for Bar Raisers — always traceable to original source feedback and never a hiring recommendation.
Interviewer Workspace · Replaced a legacy recruiter-oriented landing page with a purpose-built destination for every interviewing task, from feedback submission to training requirements.
Outcome
Interviewer Workspace became Amazon's ATS' most-visited feature after launch, with strong adoption and retention across a 500,000+ business-user base. Interview Transcription covered 6,296 interviews in its first two Early Adopter months, with 90% candidate consent authorization, more than 99% reliability, and strong interviewer sentiment: 88% said it was easy to use and 81% reported meaningful time savings. One example quote from an Amazon interviewer:
“I really appreciate the transcription tool and think it's an excellent addition to Amazon's interview process. It has been my practice at Amazon during interviews to take notes almost verbatim; I can type quite fast and it's not particularly difficult for me to both take notes and ask questions in real time. […] I quite like the prospect of being able to free up my hands and the relevant part of my brain to focus more on the candidate, the interview, and their answers to my question. Bottom line: hugely appreciate this function. Awesome job!”
The Debrief Prep Experience piloted with Bar Raisers in Q1 2026, leading to expansion toward general availability for Amazon’s 10,000 Bar Raisers. Early feedback on the Feedback Builder also showed gains in analytical depth, coherence, and projected time savings.
Reflection
This set of projects were being developed ad-hoc as they were prioritized quarterly. My design team did as much as possible to treat them as a holistic initiative and mitigate any conflicts in decision-making across teams, however if we had had more capacity, we could have used this as an opportunity to push for and establish formal interaction patterns and guardrails for AI components in the recruiting tool set. Instead, the work of aligning design patterns and standards stayed as manual processes which required additional effort to maintain consistency over the course of the projects and into the future.