Product Design Lead

Requirements

• 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 foundation, with the ability to operate at both strategic and hands‑on levels
• Hands‑on experience with AI tooling for design and development, with concrete fluency in prompt design for component generation, agent/MCP integration, and authoring evals for generated UI
• Experience working closely with engineering teams and a strong understanding of implementation constraints
• Proven ability to introduce new tools, workflows, and ways of working at team or organizational scale, and to drive adoption of them
• Systems thinking with a pragmatic approach to balancing consistency and speed
• Comfort operating in ambiguity and shaping new areas
• (Desirable) Familiarity with front‑end technologies and component‑driven development
• (Desirable) Experience building internal tools, platforms, or design infrastructure
• (Desirable) Track record of influencing senior stakeholders and driving cross‑team alignment

• 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
• 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…