AI Experience Designer / Agentic UX Designer
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- Understand people, tasks and the unique failure modes of AI interactions
- Design delegation, control and trust for agentic workflows
- Evaluate and scale AI-native experiences with measurable quality
Benchmark: OpenAI Product Designer, Codex + Product Designer, Identity (current Sep 2026) Market refresh: AI-first interaction models, delegation and control, human-agent trust, permissions, uncertainty/provenance, conversational and tool-use UX, failure recovery, rapid prototyping, design systems and evaluation of agent experience quality.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Understand people, tasks and the unique failure modes of AI interactions
User research + task/job modeling for AI workflows
Coverage: Product Discovery / UX Research / Human-Centered AI
Interaction design, usability + inclusive AI
Coverage: UI/UX Design / Usability / Accessibility
Design delegation, control and trust for agentic workflows
Agent interaction patterns, delegation + interruptibility
Coverage: AI Agents / Conversational UX / Prototyping
Explainability, trust calibration + uncertainty
Coverage: Responsible AI / Explainability / Human-AI Interaction
Evaluate and scale AI-native experiences with measurable quality
AI experience evaluation + failure-mode testing
Coverage: AI Evals / UX Research / Responsible AI
AI-native prototyping, design systems + experimentation
Coverage: Design Systems / Prototyping / Experimentation
Role-Readiness Proof / Capstone
Design an agentic workflow for a consequential real-world task. Start with user research and journey mapping; prototype the human-agent interaction; make system status, provenance, permissions, memory and uncertainty visible; define confirmation/approval and interrupt/recovery patterns; test at least three failure modes; run usability sessions and an AI evaluation set; then iterate the design using measurable task-success, trust, error-recovery and adoption evidence.
Proof Rubric
- Show evidence-backed user needs
- clear delegation and control
- calibrated trust rather than overconfidence
- understandable permissions and provenance
- accessible interaction design
- graceful uncertainty and failure recovery
- human approval at meaningful risk boundaries
- high-fidelity prototype quality
- usability and AI-eval evidence
- measurable iteration
- and design-system patterns reusable across multiple agent surfaces