Software Engineer - Frontend
Alignerr
full-remotemidcontractfrontend United States 3 days ago via LinkedIn
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JavaScriptReactVue.jsAngularHTMLCSSAccessibilityWeb PerformanceAI TrainingAsync/Await
About the role
Role Overview
You’ll be a Frontend Software Engineer on an AI Training Contract, helping make AI-generated code smarter. Instead of building product features, you’ll review and improve AI-generated frontend solutions so models learn to write more correct, efficient, accessible, and clear JavaScript/HTML/CSS.
Responsibilities
- Review and evaluate AI-generated frontend code for:
- correctness
- efficiency
- accessibility
- clarity
- Write high-quality UI/web solutions across varying difficulty levels.
- Produce clear explanations of design decisions, code logic, and problem-solving strategies.
- Identify edge cases, bugs, and ambiguities in AI outputs and in problem statements.
- Help define and maintain the standard of “great frontend code” for the training workflow.
Requirements
- Proficient in JavaScript, including:
- async/await
- closures
- DOM manipulation
- ES6+ syntax
- browser APIs
- Experience with modern frontend frameworks/tools such as React, Vue, or Angular.
- Strong written communication in English; able to explain technical concepts clearly and concisely.
- High attention to code quality, edge cases, and failure points.
- Self-motivated and comfortable working independently in an asynchronous environment.
Nice to Have
- 3–5+ years of professional frontend development experience.
- Background in computer science or a related field.
- Experience with data labeling, RLHF, or AI training workflows.
- Familiarity with web performance optimization and accessibility standards.
Work Style
- Fully remote, asynchronous contract work available from anywhere (US listed location).
About Alignerr
Alignerr is an AI-focused organization running a training initiative to improve how AI models generate and understand frontend code. The work centers on evaluating and refining AI-produced JavaScript, HTML, and CSS to produce clearer, safer, and more reliable outputs at scale.
Scraped 4/19/2026