UX Product Designer (Networking)
HybridAll, Catalunya, Kingdom of Spain
Job Summary
Own a defined product area within HPE to assist with self-driving workflows, automated actions, and assurance dashboards partnering closely with PMs and engineers. Run AI-scaled qualitative research using tools like User Intuition and Perspective AI to automate recruiting and adaptively probe users, then synthesize unstructured data into converged insights. Guide AI agents to generate production-ready UI and build prototypes in working front-end code, investing manual craft in the 10% of the experience that makes a product feel magical. Define evaluation rubrics for AI features to make experience quality measurable and support the path toward scaled evaluation. This hybrid role is based in Cupertino, CA, with two days per week in the office.
Required Qualifications
- Minimum of 2 years of product design experience
- Strong foundations in visual design, interaction design, and design systems
- Portfolio showing breadth of exploration and articulated trade-offs
- Hands-on fluency (or demonstrated fast learning) with at least one AI design-to-code / vibe design tool (e.g., VS Code, Claude Design, Lovable, v0, Cursor, Replit, Bolt, Stitch)
- Hands-on fluency (or demonstrated fast learning) with at least one AI research or analytics tool (e.g., Dovetail, Perspective AI, Fullstory, Mixpanel)
- Comfort working with data: you can read a funnel, question a metric, and let evidence guide your design decisions
- A builder's temperament: bias toward working prototypes over static specs, curiosity over fear when tools break, and pride in the details that make an experience feel crafted rather than generated
- Onsite role with expectation to primarily work from an HPE office
- Hybrid role based in Cupertino, CA
- Expected in the office 2 days per week to collaborate in person with your product, engineering, and design partners
Desired Qualifications
- Experience designing for AI/agentic products (chatbots, copilots, generative UI)
- Exposure to evaluation practices for AI systems (rubrics, golden datasets, LLM-as-a-Judge)
- Familiarity with front-end fundamentals (React, Tailwind, component architecture) — enough to guide and review agent-generated code
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