JavaScript Engineer

AI Training Specialist

Minimum 2+ years of professional JavaScript experience in production environments.

Strong understanding of closures, async/await, prototype-based inheritance, and JavaScript event loop behavior, with deep proficiency in modern Vanilla JavaScript (ES6/ES2015+).

Proven ability to evaluate correctness, readability, and performance of JavaScript code, including identifying subtle logic and edge-case issues.

Hands-on work experience in software engineering, preferably in roles involving code review, mentoring, or technical design discussions.

Significant experience using LLMs or AI coding assistants while programming, combined with a critical mindset for validating their output.

Excellent English writing skills, capable of producing clear, concise, and pedagogical technical explanations.

Strong proficiency with at least one major frontend framework (such as React, Angular, or Vue.js) or backend runtime (such as Node.js with Express or NestJS).

Minimum Bachelor's degree in Computer Science or a closely related technical field.

Previous experience with AI data training, data annotation, or evaluation of AI-generated technical content is a strong plus; minimum C1 English proficiency and a highly detail-oriented working style are required.

Preferred: contributions or merged PRs in JavaScript or TypeScript open-source projects, familiarity with bundlers and tooling (Webpack, Vite, Rollup), frontend–backend integration and Node.js runtime behavior, reviewing typed API layers, DTOs, or schema-driven designs, and experience evaluating frontend logic, async patterns, and state management.

Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.

Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.

Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.