Bash Team Lead

In this hourly, remote contractor role, you will work as a Bash Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Bash/shell scripting AI training projects. You will review AI-generated Bash code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that contributors follow expected quality standards.

You will assess work for script correctness, command safety, portability, debugging accuracy, error handling, security awareness, readability, maintainability, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Bash quality leadership will directly help improve the world’s premier AI models by ensuring that shell-scripting training data is accurate, executable, safe, clearly explained, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Your profile

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, IT, Systems Administration, or equivalent professional experience.
• Strong grasp of English to follow guidelines and provide clear technical feedback.
• 3+ years of experience with Bash/shell scripting, Linux/Unix systems, automation, DevOps, systems administration, CI/CD, or technical support workflows.
• Strong understanding of Bash fundamentals: variables, quoting, conditionals, loops, functions, pipes, redirection, exit codes, signals, process management, file permissions, and POSIX portability.
• Ability to identify issues such as unsafe commands, poor quoting, non-portable syntax, missing error handling, incorrect assumptions, hallucinated flags, and destructive operations.
• Familiarity with tools such as grep, awk, sed, find, xargs, curl, jq, cron, SSH, Git, Docker, CI/CD pipelines, and Linux package managers is preferred.
• Experience leading or supporting remote teams of trainers, reviewers, engineers, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and PM systems.
• Highly detail-oriented and organized; able to maintain style guides, FAQs, trackers, honeypots, calibration tasks, and onboarding materials.
• Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based review is a strong plus.

Key responsibilities

• Quality monitoring: Spot-check Bash items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Code review: Evaluate AI-generated shell scripts, CLI workflows, automation snippets, debugging responses, and explanations for correctness and safety.
• Trainer/QA communication: Update contributors on Discord about new guidelines, workflow changes, and Bash-specific review expectations.
• Question handling: Respond to questions around quoting, portability, Linux commands, destructive operations, permissions, pipelines, and rubric interpretation.
• Activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Documentation: Create and maintain Bash style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding: Run onboarding/training calls explaining workflows, rubrics, safety expectations, and Bash-specific review rules.
• Safety review: Flag unsafe scripts, destructive commands, credential exposure, injection risks, or misleading production guidance.
• Process improvement: Identify recurring gaps and build scalable QA processes.