Why pre-screen AI experience designers before the portfolio review
The interesting design problem is not the interface when the model is right. It is what happens when the output is confidently wrong: whether the user can tell, whether they can correct it, and whether the interface encouraged more trust than the system deserves. Designers worth hiring lead with that. A short screen asks how their interface handles a wrong answer.
What actually matters when screening AI Experience Designer candidates
- 01
Portfolio
Review shipped AI interfaces: chat assistants, copilots, recommendation surfaces. Ask what they designed for confidence display, citation, fallback states, and how usage or task success changed.
- 02
Craft and rationale
Probe craft in probabilistic UX: error and hallucination states, streaming responses, prompt affordances, model latency handling, Figma prototypes wired to real or mocked LLM output.
- 03
Feedback and iteration
Assess how they tested AI features: wizard-of-Oz sessions, red-teaming prompts with users, reviewing conversation transcripts, and what they changed after seeing users mistrust or over-trust output.
- 04
Working with the brief
Look for work with ML engineers and product on what the model can actually do: dataset limits, evaluation criteria, responsible AI or transparency guidelines they helped write.
Pre-screening questions to ask AI Experience Designer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Shipped AI features
3 questions01Can you describe an AI project you contributed to and your role in it?
Listen forA shipped feature with their contribution described, and what users actually did with it.
Concept work presented as product, or no knowledge of how the feature performed in use.
02Have you redesigned an AI interface based on user feedback?
Listen forA specific change driven by observed misuse or misplaced trust, with the result described.
Feedback collected without changes, or redesigns limited to visual refinement.
03What challenges have you faced designing these experiences?
Listen forReal difficulties such as inconsistent output, latency or explaining limits to users.
Challenges described as stakeholder expectations, or no problem specific to model behaviour.
Designs for error
4 questions04What is your process for designing interfaces for these systems?
Listen forError and uncertainty states designed alongside the successful path from the beginning.
Design starting from the ideal output, or failure states added at the end.
05How do you handle a feature that does not produce the expected results?
Listen forCorrection and escape routes built in, with users able to override or ignore the output easily.
Users left with no way to correct output, or errors handled by hiding the feature.
06What steps do you take to make complex systems understandable to users?
Listen forCapability communicated honestly, with confidence conveyed in a way that matches reality.
Interfaces that imply certainty, or explanations that overstate what the system does.
07How do you ensure a system provides a genuinely useful personalised experience?
Listen forPersonalisation that users can see and adjust, with the cold start problem designed for.
Personalisation applied invisibly, or no way for users to correct wrong assumptions about them.
Tested on real output
2 questions08What methods do you use to evaluate the usability of these designs?
Listen forTesting with genuine model output including failures, watching whether users notice errors.
Testing with curated examples, or evaluation limited to the interface without the model.
09How do you measure the success of one of these experiences?
Listen forTask success and appropriate reliance measured, not just engagement with the feature.
Success measured by usage alone, or over-reliance on wrong output never examined.
Works with the model team
3 questions10Can you describe collaborating with data scientists or engineers?
Listen forDesign constraints negotiated with the model team, including accuracy targets and latency.
Designs handed over without discussion, or model limitations discovered during build.
11How do you ensure these systems are accessible to all users?
Listen forAccessibility considered broadly, including for the users that training data typically underrepresents.
Accessibility limited to interface standards, or model performance gaps not treated as an access issue.
12How do you handle ethical considerations in these designs?
Listen forDisclosure, consent and the risk of manipulation all addressed, with something they refused to design.
Ethics described as a principle, or no design decision they have declined on those grounds.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Portfolio
35%5Shows live AI products with before and after task success or trust metrics, plus the flows they personally owned.
Craft and rationale
25%5Explains design choices in terms of model behaviour, uncertainty, and user recovery paths rather than visual preference alone.
Feedback and iteration
25%5Cites specific transcript or usability findings that forced a redesign, including features they removed or gated.
Working with the brief
15%5Translates model constraints into scoped design decisions and negotiates feasibility with engineers using shared evaluation language.
The design problem is what happens when the model is confidently wrong. A one-way video screen asks about that.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async alongside a portfolio. Enough to test how they design for model error before anyone reviews case studies in depth.
How technical does this designer need to be?
Enough to understand what the model can and cannot do, and to discuss confidence and failure rates with engineers. Otherwise the design will promise capability that does not exist.
Evaluating answers
What is the strongest signal when screening this role?
How the interface handles a wrong answer. Designers who shipped describe correction paths and calibrated confidence. Anyone who designs for the successful case has not met real users.
How do I judge their testing?
Ask what they tested with. Real answers involve genuine model output including failures. Anyone testing with curated examples has evaluated a demonstration rather than a product.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
