Anthropic UX Designer Interview Guide

Define test objectives, create task scenarios, recruit representative users (5-8), use think-aloud protocol, observe without intervening. Track task success rate, time on task, error rate, SUS scores, and qualitative feedback. Discuss remote vs in-person testing trade-offs.

Discuss lightweight methods - guerrilla testing, internal user feedback, analytics data, competitor analysis, stakeholder interviews, design desk research, and leveraging existing research. Show resourcefulness while acknowledging limitations. Emphasize quick validation loops.

Use affinity diagramming to group findings, identify patterns and themes, create personas or journey maps, prioritize insights by impact/frequency, translate into design principles or requirements. Discuss collaborative synthesis workshops and stakeholder alignment.

Qualitative explores "why" through interviews, observations (small sample, rich insights). Quantitative measures "what" through surveys, analytics (large sample, statistical significance). Use qualitative for exploration/understanding, quantitative for validation/measurement. Combine for triangulation.

Use paper prototypes or low-fidelity mockups for quick feedback, conduct concept testing with users, create click-through prototypes for flows, run usability testing on key tasks, measure comprehension and task success. Discuss iteration based on findings.

Understand user goals and pain points, map current vs desired state, create progressive disclosure strategy, design empty states and first-time user flows, provide contextual help, measure time-to-value. Discuss balancing comprehensiveness with simplicity and different user personas.

Conduct content audit, create card sorting exercises with users, develop site map and navigation taxonomy, design findability mechanisms (search, filters, breadcrumbs), validate with tree testing. Discuss mental models and ensuring scalability for future content.

Start with mobile constraints (small screen, touch targets), prioritize core user flows, use progressive enhancement for larger screens, consider context of use, maintain consistent patterns but adapt layouts. Discuss touch vs hover interactions and navigation patterns.

Follow WCAG guidelines, ensure keyboard navigation, use semantic HTML, maintain sufficient color contrast (4.5:1 minimum), provide alt text, design for screen readers, test with accessibility tools. Discuss inclusive design principles and testing with assistive technologies.

Audit existing patterns, define core principles, create component library with variants/states, document usage guidelines, establish governance model, version control, ensure developer handoff clarity. Discuss balancing consistency with flexibility and scaling the system.

Use case study structure - problem statement, research insights, design goals, iterations, final solution, metrics/impact. Focus on your individual contributions, design rationale, constraints faced, and learnings. Quantify results when possible. Show work in progress, not just polished screens.

Set up initial assumptions/hypothesis, explain research method, share surprising insights, show how design pivoted based on data, discuss impact of changes. Demonstrate humility and user-centricity over attachment to initial ideas.

Present design rationale with user data, understand stakeholder concerns and constraints, find compromise solutions, propose A/B testing to validate, maintain user advocacy while being pragmatic. Show examples of successful negotiation and building trust.

Use impact/effort matrix, identify high-value problems affecting most users, consider business goals alignment, quick wins vs long-term improvements, data-driven prioritization. Discuss stakeholder alignment and communicating trade-offs clearly.

Conduct research with target audience, avoid assumptions, involve users throughout design process, learn about cultural contexts and accessibility needs, test with representative users. Show empathy, cultural sensitivity, and commitment to inclusive design.

Define success criteria upfront tied to business/user goals. Track task completion rate, time on task, error rate, satisfaction scores (NPS, SUS), adoption rates, engagement metrics. Discuss qualitative feedback, A/B test results, and iterating based on data.

Give specific, actionable feedback focused on goals/users rather than personal preference. Use "I" statements, ask questions to understand rationale. Receive feedback with openness, separate ego from work, ask clarifying questions. Discuss creating psychologically safe critique culture.

Involve PM and engineers early in process, understand technical constraints, create detailed specs with annotations, use shared design systems, provide interactive prototypes, be available during implementation. Discuss design QA and iteration based on technical feedback.

Approach diplomatically, provide side-by-side comparison with specs, understand if there were technical constraints, collaborate on solution, document decisions. Discuss improving handoff process and building better relationships for future projects.

Start with business context and user problem, focus on outcomes over process, use storytelling, show concrete examples, quantify impact, be concise, anticipate questions, provide multiple options with recommendations. Discuss adapting presentation style to audience.

Clarify constraints (target users, platform), identify user goals and pain points, research existing solutions, sketch multiple concepts, select direction with rationale, detail key screens/flows, discuss success metrics. Show structured thinking and ability to work through ambiguity.

Understand current pain points (availability, insurance, location), define user goals (convenience, trust, information), sketch user flow, design key moments (search, booking, confirmation), consider edge cases (cancellation, rescheduling). Show empathy for healthcare context.

Identify motivations (behavior change, accountability), design simple logging interface, create visual progress feedback, use gamification appropriately, design reminders/notifications, consider social features. Discuss behavior design principles and sustained engagement.

Research tourist needs (landmarks, navigation, recommendations), design curated guides, improve offline functionality, add AR features for wayfinding, integrate reviews and photos. Discuss balancing new features with core navigation experience and measuring success.

Understand discovery challenges (filter bubble, content overload), design personalized recommendations, create content categories/topics, improve search and browse experience, add curation mechanisms. Discuss algorithmic vs editorial approaches and measuring engagement quality.

Identify misinformation patterns, design contextual warnings, add fact-checking indicators, improve source transparency, create reporting mechanisms. Discuss ethical considerations, balancing speed with accuracy, and measuring effectiveness without censorship.

Reduce pressure of perfection, design ephemeral content features, create private sharing options, de-emphasize likes/metrics, add creative tools. Discuss tension between authenticity and engagement metrics and running experiments to validate changes.

Address verification, reviews/ratings, communication, insurance/protection, dispute resolution. Design transparent profiles, secure messaging, clear cancellation policies. Discuss building trust in two-sided marketplace and measuring safety perception.

Create mood/activity taxonomy, design selection interface, personalize recommendations, integrate with playlists, add context-aware suggestions. Discuss music discovery vs exploitation and measuring discovery success beyond skips.

Show genuine, well-reasoned concern about AI safety. Explain what differentiates Anthropic's approach — constitutional AI, interpretability research, or the empirical safety approach. Avoid generic answers.

Demonstrate understanding of both approaches. Discuss how constitutional AI uses principles to guide model behavior, the advantages over pure human feedback, and the remaining challenges.

Think about behavioral testing, probing internal representations, adversarial evaluation, and the fundamental difficulty of detecting deception. Show original thinking about an open research problem.

Show that safety thinking is natural for you. Describe how you identified the risk, communicated it to stakeholders, and drove a resolution. Anthropic wants people who proactively think about failure modes.

Show nuanced thinking. Discuss Anthropic's view that building frontier models is necessary for safety research, while safety must advance alongside capabilities. Avoid simplistic positions.

Consider automated evaluation, human review pipelines, anomaly detection, and incident response. Show understanding of the unique challenges of monitoring AI systems compared to traditional software.

Anthropic's empirical approach to safety means updating beliefs based on evidence. Show intellectual humility and willingness to let data change your mind, even when it's uncomfortable.

Discuss a specific alignment problem with depth — scalable oversight, interpretability, reward hacking, or deceptive alignment. Show you've thought carefully about the problem space.

This is core to Anthropic's mission. Discuss training approaches, evaluation methods, and the fundamental challenges of ensuring AI honesty. Show understanding of current research and open questions.

Anthropic values interdisciplinary collaboration. Show how working with people from different backgrounds — safety researchers, ML engineers, policy experts — led to insights neither group would have reached alone.