Principal Design Systems Designer

Ethos Life

2h•$165,000 - $292,000•Remote

About The Position

Ethos is seeking its first Principal Design Systems Designer to establish the foundational elements that enable designers, Product Managers (PMs), and engineers to consistently create on-brand, accessible, high-quality UI. This role will also be responsible for setting up the prompt-to-production pipeline for shipping products. This is a unique opportunity to build a design system from the ground up in an AI-native way, rather than retrofitting it later. The Principal Design Systems Designer will collaborate closely with Product Design, Engineering, Brand/Creative, Content Design, and AI tooling teams to create a cohesive pipeline integrating Figma, AI coding tools (Cursor/Claude Code), and Storybook, with appropriate human oversight.

Requirements

Experience owning a design system end-to-end at a product company, including driving adoption through organizational efforts.
Deep fluency in design-to-code integration, including token pipelines, component APIs, versioning, and practical synchronization of design and code.
Proven track record of driving adoption through pragmatic, builder-friendly processes rather than heavy governance.
Strong working knowledge of modern front-end concepts (tokens, components, states, responsive behavior) and comfort collaborating with engineers on tradeoffs.
Experience setting accessibility standards and shipping accessible components, incorporating WCAG-informed practices and inclusive design thinking.
Genuine curiosity and hands-on experience with AI-assisted design and development workflows, including tools like Cursor, Claude Code, or equivalents, with a clear perspective on human involvement.
Ability to set direction for peers and partner teams, making and defending opinionated decisions on ambiguous tradeoffs (e.g., governance vs. speed, system purity vs. product reality, human review vs. agent autonomy) with evidence.
Capacity to thrive in ambiguity and build structure in nascent environments, demonstrating pragmatism, strong opinions, and an iterative approach.
Clear communication skills to align diverse stakeholders and influence without formal authority.

Responsibilities

Audit current product UI and design artifacts to identify opportunities for standardization, consolidation, and reduction of inconsistency.
Define the information architecture of the design system, including naming conventions, taxonomy, component hierarchy, and documentation structure, to ensure usability by both humans and AI agents.
Develop a detailed plan, in partnership with design, engineering, and leadership, to prioritize design system efforts, balancing long-term scalability with immediate product needs.
Define and implement a scalable token strategy (color, typography, spacing, elevation, motion) with a pipeline to synchronize Figma variables, code tokens, and Storybook.
Collaborate with Front-End engineers to translate the design system into reusable, well-architected components with clear APIs, states, and built-in accessibility.
Manage the end-to-end component lifecycle, including versioning, release notes, deprecations, and handoffs.
Establish a lightweight, builder-friendly contribution process for designers, engineers, and PMs to propose, review, and add to the system with minimal governance overhead.
Create user-friendly documentation, including guidelines, do/don't examples, usage rationale, and decision records, structured for both human and machine readability.
Drive pragmatic adoption by focusing on high-impact areas, shipping iteratively, and building credibility through visible product improvements.
Define how AI agents will assist in auditing, tagging, and maintaining the system's structure, and identify necessary human review checkpoints for critical decisions.
Shape the pipeline that transforms Figma frames or PM prompts into Storybook-validated components using AI coding tools, incorporating review checkpoints for quality, accessibility, and brand integrity.
Make the system legible to AI agents by ensuring components, tokens, and patterns are well-documented, named, and structured for effective use in AI-driven design and development.
Define success metrics and track key indicators such as adoption rates, UI consistency, design/development cycle time, accessibility improvements, and the quality of AI-generated output.
Partner with Research and Data teams to correlate system changes with product outcomes like quality, user confidence, and conversion efficiency.