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Job Description & How to Apply Below
This is not a traditional UX role. Most design assumes a human drives and software responds. You'll design the inverse: autonomous agents act, and humans supervise, intervene, correct, and trust. You'll define how staff oversee fleets of agents — what to surface, when to interrupt, how to hand control back and forth, and how to make an agent's reasoning legible enough that a human can step in confidently mid-task (live-call barge-in, supervisor consoles, exception queues, confidence-gated approvals).
You own the design language for human-agent collaboration in a regulated healthcare environment, where a misread interface has real consequences.
What You'll Do
Design supervision & intervention experiences: live monitoring, escalation, human-in-the-loop approvals, and graceful agent↔operator handoff.
Create patterns for agent legibility — communicating intent, confidence, state, and rationale so
humans can trust or override.
Prototype in code (React/Type Script). You ship working interfaces, not just mockups.
Partner with engineering on real-time data surfaces (Web Socket event streams, observability) on our AWS serverless stack.
Design for PHI-sensitive, multi-tenant contexts where role, access, and audit trails shape the UI.
Establish the interaction grammar for new Agentic Flow products and our customer-facing portal.
What We're Looking For
6+ years in UX / product design or design engineering, with shipped, production-grade work.
Fluency designing and building — comfortable in React/Type Script, translating designs into functioning prototypes.
Strong systems thinking — you reason about state machines, edge cases, and failure modes,
not just happy paths.
Experience with complex B2B/operator tools, data-dense interfaces, or safety-critical UX.
A portfolio is required. Show how you've solved hard interaction problems — bonus for
dashboards, operational tooling, real-time systems, or AI/agent interfaces.
Nice to Have
Healthcare, fintech, or other regulated-domain experience.
Familiarity with conversational/voice AI, LLM-driven products, or observability tooling.
Background designing for trust, explainability, or human-AI teaming.
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