PHP QA Lead - Remote
About the role
This is an hourly, remote contractor role as a PHP Quality Assurance Lead. The role involves overseeing quality, consistency, and trainer performance across PHP AI training projects. You will review AI-generated PHP code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards.
Responsibilities
- Spot-check PHP items and provide ongoing feedback through DMs.
- Review AI-generated PHP code, backend snippets, debugging responses, API examples, database logic, and tests.
- Update trainers/QAs on Discord about guideline changes and PHP-specific quality standards.
- Answer questions around PHP syntax, frameworks, runtime behavior, security, testing, and rubric interpretation.
- Activate inactive contributors through DMs and follow-up tracking.
- Create and maintain PHP documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
- Run onboarding/training calls for PHP trainers and QAs.
- Flag insecure, outdated, misleading, or non-production-ready PHP recommendations.
- Identify recurring quality gaps and improve QA processes.
Requirements
Strong PHP expertise, English communication skills, attention to detail, and the ability to manage quality workflows across remote technical teams are required. Bachelor’s or Master’s degree in Computer Science, Software Engineering, IT, or equivalent professional experience is preferred. Strong understanding of PHP 8+, OOP, namespaces, Composer, autoloading, arrays, exceptions, type declarations, sessions, HTTP, REST APIs, database access, and error handling is essential. Familiarity with Laravel, Symfony, PHPUnit, Pest, MySQL/PostgreSQL, Redis, Docker, GitHub, CI/CD, static analysis tools, and PSR standards is preferred. Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, or QAs is strongly preferred. Highly organized and able to maintain documentation, style guides, trackers, FAQs, calibration tasks, and honeypots is necessary. Experience with AI training, LLM evaluation, code QA, or rubric-based review is a strong plus.