Frontend Product Software Engineer, Design Systems

Remote - MexicoFull-TimeSenior

Salary not disclosed

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Job Details

Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.
5+ years of experience in front-end development, including experience contributing to or maintaining a design system or shared UI platform.
Strong proficiency in HTML, CSS, and JavaScript, with deep experience in modern frameworks (e.g., React) and component-driven development.
Demonstrated experience building scalable, maintainable front-end architecture for complex applications.
Hands-on experience integrating or building with AI/ML-powered tools (e.g., LLM APIs, agent frameworks, or automation pipelines) in a development workflow.
Solid understanding of accessibility standards (WCAG, ARIA) and how to implement them in reusable systems.
Proven ability to collaborate effectively with cross-functional partners (design, product, engineering) and communicate technical concepts clearly.

Develop, maintain, and evolve UI components and primitives that power Dropbox’s design system, enabling scalable and consistent product development.
Design and build AI-powered tooling and agent-driven workflows that accelerate design-to-code processes and improve developer productivity.
Partner closely with designers to translate design intent into high-quality, reusable code, while also shaping how AI can assist or automate parts of this workflow.
Contribute to the architecture and strategy of AI-native UX development, including the integration of LLMs, agents, and automation into internal tooling.
Prototype and productionize new concepts that explore the future of design systems.
Ensure accessibility and inclusivity by embedding WCAG standards and best practices into both components and AI-assisted workflows.
Write clear, scalable documentation for both human and AI consumers (e.g., enabling agents to effectively use the design system).
Collaborate in code reviews, design critiques, and technical discussions to continuously raise the quality bar.
Troubleshoot and resolve issues from internal teams, improving system reliability and usability.
Drive experimentation and evaluation of emerging AI technologies, translating insights into practical improvements in tooling and workflows.

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