Product Design Lead

Role Overview

We're hiring a Product Designer to lead the next chapter of our design system and define how AI agents are used to design and build product features. This is a platform and enablement role: you raise the quality and velocity of every team by evolving the system, the guidance around it, and the AI harness that teams use - not by designing features yourself.

You'll work at the intersection of design, engineering, and AI - setting direction, shipping practical tooling, and helping product teams design and build with confidence on top of what you provide.

• It is: owning the design system, the guidance and patterns around it, the adoption motion across teams, and the AI agent harness that teams use to generate feature designs.
• It is not: designing individual features, screens, or flows on behalf of product teams. Those teams design their own features using your system and AI tooling - your job is to make that path clear, fast, and high-quality.

Evolve the design system and guidance

• Define and drive the long-term vision and strategy for the design system across products.
• Own and evolve system foundations - components, tokens, patterns, layouts - to support scale and consistency.
• Author the guidance layer that turns the system into good decisions: usage guidelines, do’s and don’ts, content and interaction patterns, accessibility expectations, and design principles.
• Make the system machine-readable: structured component metadata, tokens-as-API, and MCP-compatible documentation so AI agents and humans consume the same source of truth.
• Define a clear contribution model so teams can extend the system without forking it.

• Treat adoption as a product. Understand where teams struggle, lower the cost of doing the right thing, and remove reasons to go off-system.
• Run the enablement motion: documentation, onboarding, office hours, design reviews, paired sessions, and internal training.
• Partner with product, design, and engineering leads to embed the system into their planning, review, and shipping workflows.
• Define and track adoption and quality metrics (component coverage, off-system usage, design-to-code drift, accessibility conformance) and report on them.

Shape and ship the tooling that lets product teams use AI agents to generate feature designs and front-end implementations that conform to our system by default. You define the harness; teams use it to do their own design and build work.

• Identify high-impact AI use cases that improve team velocity and quality, and prioritize which to operationalize.
• Build and operationalize practical AI harnesses that teams use directly, including:
  • Rapid prototyping and generation of feature designs and variants from briefs.
  • Design-to-code alignment and implementation verification against system components.
  • Automated design validation and consistency checks (system conformance, accessibility, content rules).
• Author the eval set and guardrails that decide when AI-generated UI is on-system, accessible, and ready to ship - so quality is enforced continuously, not by manual review.
• Define the contracts, prompts, and feedback loops that keep AI-generated output on-system and on-brand.
• Partner with engineering on the underlying infrastructure (component metadata, model integrations, evaluation harness, agent integrations such as MCP).

• Establish quality standards and introduce scalable, automated design QA that runs continuously rather than as a manual gate.
• Improve collaboration between design and engineering: handoff, documentation, shared ownership of components, and tighter feedback loops.
• Mentor product engineering teams and influence cross-functional stakeholders to raise the overall quality bar - through systems, tooling, and rituals rather than by taking on their work.

• Strong experience leading or scaling design systems in a multi-team environment.
• A platform and enablement mindset - you measure your impact by what other teams ship, not by what you design yourself.
• Solid product design…