Senior Bash Code Reviewer

About OpenTrain

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About AI Training Work

AI training (data labeling/annotation) is the human side of building modern AI systems. Contributors review, rate, and correct model outputs—work that is often remote, flexible, and accessible to people with domain expertise rather than formal AI credentials.

This role focuses on evaluating and improving the quality of training data: your reviews and feedback directly shape how shell-script-generating models behave in production.

The Role

You will act as a senior Bash code reviewer auditing annotator reviews of AI-generated shell scripts. For each submission you must validate the script in an isolated sandbox, confirm it satisfies the prompt, runs correctly across POSIX-compliant shells, and follows best practices for security and reliability.

When annotator reviews do not meet the rubric, you will correct ratings and deliver clear, actionable feedback so that each reviewed example meets quality standards for use in training next-generation AI models.

What You'll Do

Perform hands-on validation of AI-generated shell scripts in sandboxed VMs or containers, ensuring correct behavior and prompt compliance.

Evaluate cross-shell compatibility (POSIX sh compatibility and Bash 4.x/5.x), security practices, error handling, and idempotence.

Correct mis-ratings from annotators and provide concise, constructive feedback tied to the project rubric.

• Execute and test scripts safely in isolated environments (VMs or containers).
• Verify safe handling of inputs, proper quoting, and avoidance of unsafe eval or insecure sourcing.
• Check for robust error handling, sensible exit codes, and re-runnable logic.
• Ensure scripts follow least-privilege principles and secure file handling.
• Write clear feedback and examples to help annotators improve future reviews.

Requirements

Candidates must meet the stated experience and technical skills below — these are essential to perform thorough, reliable reviews.

• 5–7+ years professional experience in Bash scripting, Linux systems administration, or dedicated code-review roles.
• Expert knowledge of Bash 4.x/5.x and POSIX sh compatibility, including subshells, pipelines, traps, and environment configuration.
• Proven ability to spot unescaped variables, injection risks, unsafe eval/sourcing, and insecure file operations.
• Experience implementing robust error handling and idempotent scripts (familiarity with set -euo pipefail and sensible exit codes).
• Proficiency with testing and debugging tools such as Bats, shellcheck, and bash -x tracing.
• Familiarity with cron jobs, systemd units, and containerized Linux environments.
• Comfort executing and validating scripts in isolated VMs or containers to verify prompt compliance.
• Structured QA experience: rubric-based scoring, checklist reviews, and ticketing/annotation tools (e.g., Jira, Asana).
• Strong written English communication (B2+ CEFR) for clear feedback and mentoring.

Nice To Have

These are not required but will make you a stronger candidate.

• Exposure to LLM evaluation, RLHF pipelines, or prior AI/ML data-labeling projects.
• Experience mentoring reviewers or improving annotation rubrics and quality processes.

Logistics, Pay, and How It Works

This is a remote, contract, part-time role requiring 20+ hours per week. Open to candidates worldwide. You will work through OpenTrain’s platform and project tooling and may use other designated annotation software.

Compensation is pay-per-hour at USD 21/hour. You must be comfortable performing tests in isolated environments and following project-specific rubrics and workflows.

• Employment type: Contractor, Part-time.
• Time commitment: 20+ hours/week.
• Pay: USD 21 per hour (PAY_PER_HOUR).
• Data type: Computer code / programming; label types: computer programming / coding.
• (project-specific tools).

Who Should Apply

Apply if you are a seasoned Bash engineer who enjoys hands-on debugging, security-minded scripting, and mentoring reviewers to raise annotation quality. This role suits professionals who want flexible, remote work that directly influences AI behavior and model training data.