In this hourly, remote contractor role, you will work as a Computer Science & IT Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across computer science, IT, software, systems, and technical AI training projects. You will review AI-generated computer science/IT content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards. You will assess work for technical accuracy, conceptual correctness, code and systems reasoning, terminology quality, troubleshooting logic, security awareness, clarity, 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 requires strong computer science/IT expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams. 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 computer science and IT quality leadership will directly help improve the world’s premier AI models by ensuring that technical training data is accurate, logically sound, clearly explained, well-documented, and aligned with client expectations.
requirements
- Bachelor’s, Master’s, or equivalent professional experience in Computer Science, Information Technology, Software Engineering, Information Systems, Cybersecurity, Data Science, or a closely related technical field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
- 3+ years of experience in software development, IT support, systems administration, networking, cybersecurity, data workflows, technical education, technical writing, QA, or related technical-review workflows.
- Strong understanding of computer science and IT fundamentals, including programming concepts, algorithms, data structures, operating systems, databases, networking, cloud basics, security principles, APIs, debugging, and troubleshooting.
- Ability to evaluate technical content against detailed rubrics and identify issues such as incorrect code logic, flawed troubleshooting steps, hallucinated commands/APIs, security risks, wrong terminology, unsupported assumptions, or incomplete explanations.
- Familiarity with common tools and environments such as Git/GitHub, IDEs, terminals, Linux/Windows/macOS, SQL, cloud platforms, REST APIs, Docker, networking tools, and ticketing/support systems is preferred.
- Experience leading or supporting remote teams of engineers, IT specialists, reviewers, annotators, trainers, educators, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
- Experience with AI training, data annotation, LLM evaluation, technical QA, code review, IT knowledge review, or rubric-based review is a strong plus.
responsibilities
- Quality monitoring: Spot-check computer science and IT items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Technical review: Evaluate AI-generated explanations, code snippets, troubleshooting steps, system-design content, database guidance, networking answers, security-related content, and technical reasoning for accuracy and clarity.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and technical review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around programming concepts, debugging, IT workflows, system behavior, networking, databases, security, and rubric interpretation.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
- Documentation: Create and maintain computer science/IT project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and technical review requirements.
- Quality alignment: Ensure all trainers and QAs apply computer science/IT guidelines consistently and understand updates as projects evolve.
- Risk review: Flag misleading, insecure, non-executable, hallucinated, outdated, or technically unsafe recommendations.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for computer science and IT AI training projects.
- Native fluency in Punjabi
