Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 5
API
Communication @ 6
Debugging @ 3
Internationalization @ 3
Observability @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Stripe is a financial infrastructure platform for businesses. The UI Platform team, known as Sail, builds the foundations supporting Stripe’s Dashboard and other user interfaces. The team owns Stripe’s design system and develops reusable components, design tokens, theming capabilities, documentation, and developer tooling.
The team is building Sail for Agents, including generation infrastructure, documentation systems, agent integrations, and automated quality checks for AI coding agents. It is also evolving the visual language of the Dashboard while modernizing its underlying codebase.
Responsibilities
- Take end-to-end ownership of software projects, independently scoping problems and carrying solutions through design, implementation, testing, and rollout.
- Build and maintain accessible, performant, and well-tested components, themes, and frontend APIs used across Stripe.
- Build tooling, documentation, and automated quality checks that help engineers and AI coding agents use Sail effectively and meet Stripe’s UI quality standards.
- Partner with product designers and engineers to understand their needs and turn recurring UI patterns into reusable design-system capabilities.
- Help evolve the visual language of Stripe’s Dashboard while modernizing its underlying codebase and migrating legacy UI patterns.
- Improve the accessibility, internationalization, observability, performance, reliability, and developer experience of the design system.
- Contribute code reviews, technical discussions, and documentation that improve the quality of the team’s work.
Requirements
Minimum Requirements
- Fluency in using AI-assisted and agentic development tools as part of day-to-day software engineering, including critically reviewing, validating, and improving generated code.
- At least 2 years of professional frontend engineering experience, or equivalent practical experience, building, shipping, and debugging web applications.
- Strong knowledge of modern frontend technologies and frameworks, including browser fundamentals, responsive design, and accessibility.
- Ability to independently scope and deliver open-ended frontend projects, making practical technical and product tradeoffs with limited oversight.
- Strong problem-solving and debugging skills, with attention to testing, maintainability, and user experience quality.
- Strong written and verbal communication skills and the ability to collaborate effectively with designers, product engineers, and other partners.
Preferred Qualifications
- Experience working on a design system, frontend platform, or shared component library.
- Experience with developer tooling such as build tools, package managers, codemods, lint rules, or large monorepos.
- Experience building tools, integrations, or automated evaluation and quality systems for AI coding agents.
- Experience using documentation, developer feedback, or adoption data to improve a developer-facing product.
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