PHP Team Lead, Quality Assurance

This PHP Team Lead role focuses on PHP quality assurance across AI training projects. Review the duties and qualifications below before applying, as the role combines team leadership with detailed PHP quality review.

You will assess AI-generated code and trainer QA work, give clear technical feedback, maintain project guidance, and help trainers and QAs improve their work.

• Review PHP code, backend snippets, debugging responses, API examples, database logic, and tests.
• Check correctness, runtime behavior, performance, security, test coverage, and instruction-following.
• Identify insecure SQL handling, weak validation, flawed logic, outdated syntax, hallucinated APIs, and incomplete explanations.
• Answer questions about PHP syntax, frameworks, testing, and how to interpret project rubrics.
• Maintain PHP documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
• Support onboarding and training for PHP trainers and QAs, and identify recurring quality gaps.

This is remote contractor work available to candidates in the United States. The role is part time and requires at least 20 hours each week.

• Pay: $70 per hour.
• Location: Open to candidates in the United States.
• Time: 20+ hours per week.
• Experience: Intermediate level.
• Language: Professional fluency in English.
• Core skill: Strong PHP 8+ expertise, including object-oriented programming, namespaces, Composer, autoloading, exceptions, type declarations, sessions, HTTP, REST APIs, and database access.
• Workflow experience: Code review, quality assurance, mentoring, or a similar technical process.
• Preferred familiarity: Laravel, Symfony, PHPUnit, Pest, MySQL or PostgreSQL, Redis, Docker, GitHub, continuous integration and delivery, static analysis tools, and PSR standards.

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to join and helps people find AI training and data-labeling work in one place.

AI training is human work that helps computer models learn from examples, including code reviews and ratings of model responses. People with strong technical knowledge are needed to judge whether generated code is correct, secure, readable, and useful.