Bash Team Lead, Quality Assurance

You will review AI-generated Bash code and trainer quality assurance work. Your reviews will focus on correctness, safety, portability, debugging accuracy, and compliance with project rubrics.

You will also give clear written feedback, support contributor onboarding, maintain review materials, and help improve consistency across Bash and shell-scripting training projects.

• Review Bash scripts, command-line workflows, automation snippets, debugging responses, and explanations.
• Check for quoting mistakes, portability problems, destructive commands, missing error handling, and security risks.
• Evaluate trainer and QA work against project guidelines and rubrics.
• Give contributors clear feedback and help resolve recurring quality issues.
• Maintain style guides, FAQs, trackers, examples, honeypots, and calibration materials.
• Help improve QA processes and consistency among contributors.

This is a remote contractor role open to candidates in the United States. The work is expected to last one to three months and requires about 20 hours per week.

• Pay: Up to $65 per hour.
• Location: Remote and open to candidates in the United States.
• Schedule: About 20 hours per week for one to three months.
• Experience: Expert-level Bash or shell scripting skills with Linux or Unix systems experience.
• Background: Experience in automation, DevOps, systems administration, CI/CD, technical support, or code review workflows.
• Quality skills: Identify unsafe commands, poor quoting, non-portable syntax, made-up flags, and destructive operations.
• Communication: Clear written English and strong attention to detail.
• Tools: Comfort with Discord, Google Sheets, Google Docs, GitHub, dashboards, and project trackers.
• Helpful experience: AI training, data annotation, large language model evaluation, code QA, or rubric-based review.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind many AI systems, including reviewing code and rating model responses. People with specialized skills are needed to catch errors, improve safety, and help models produce more reliable results.

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