{"html":"<p>In this hourly, remote contractor role, you will work as a Python (Generalist) Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Python AI training projects. You will review AI-generated Python code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards.</p>\n<p>You will assess work for code correctness, runtime behavior, debugging accuracy, readability, maintainability, performance, security awareness, test coverage, 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.</p>\n<p>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 Python quality leadership will directly help improve the world’s premier AI models by ensuring that Python training data is accurate, executable, idiomatic, clearly explained, well-documented, and aligned with client expectations.</p>\n<p>Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.</p>\n<p>Important:\nThere 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.</p>\n<h3>Responsibilities</h3><ul><li>Spot-check Python items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.</li><li>Review AI-generated Python code, debugging responses, algorithms, backend snippets, automation scripts, tests, and explanations.</li><li>Update trainers/QAs on Discord about guidelines, workflow updates, and Python-specific quality expectations.</li><li>Respond to questions around Python syntax, runtime behavior, exceptions, package usage, testing, typing, security, performance, and rubric interpretation.</li><li>DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.</li><li>Create and maintain Python documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.</li><li>Run onboarding/training calls for Python contributors.</li><li>Flag misleading, non-executable, insecure, inefficient, or non-production-ready Python recommendations.</li><li>Identify recurring quality gaps and improve Python QA workflows.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or equivalent professional software engineering experience.</li><li>Strong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.</li><li>3+ years of professional experience in Python development, backend engineering, automation, scripting, data workflows, code review, QA, teaching, or technical mentoring.</li><li>Strong understanding of Python fundamentals such as data structures, functions, classes, modules, exceptions, comprehensions, iterators, generators, decorators, context managers, virtual environments, packaging, and testing.</li><li>Ability to evaluate Python content against rubrics and identify issues such as incorrect logic, non-executable code, poor exception handling, inefficient algorithms, unsafe file/network operations, hallucinated APIs, or incomplete explanations.</li><li>Familiarity with pytest, unittest, typing, mypy, pip, poetry, virtualenv, FastAPI, Flask, Django, requests, asyncio, pandas, SQLAlchemy, GitHub, Docker, and CI/CD is preferred.</li><li>Experience leading or supporting remote teams of trainers, engineers, reviewers, annotators, educators, or QAs is strongly preferred.</li><li>Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.</li><li>Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.</li><li>Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.</li></ul>"}