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.

What This Role Is - and Isn’t

• 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.

Key Responsibilities

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.

Drive adoption across teams

• 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.

Build the AI harness teams use to design and build features

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).

Quality, collaboration, and leadership

• 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.
• Define and drive the long-term vision and strategy for the design system across products
• Own and evolve system foundations (components, tokens, patterns) to support scale and consistency
• Drive adoption across teams while balancing standardization…