{"schemaVersion":"jobsearcher.job.v1","id":"73dfc29a389cf85d04a49a20","url":"https://jobsearcher.com/jobs/73dfc29a389cf85d04a49a20","canonicalUrl":"https://jobsearcher.com/jobs/73dfc29a389cf85d04a49a20","title":"Python Team Lead","description":"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. 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. 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. 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.\n\nrequirements\nBachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or equivalent professional software engineering experience.\nStrong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.\n3+ years of professional experience in Python development, backend engineering, automation, scripting, data workflows, code review, QA, teaching, or technical mentoring.\nStrong understanding of Python fundamentals such as data structures, functions, classes, modules, exceptions, comprehensions, iterators, generators, decorators, context managers, virtual environments, packaging, and testing.\nAbility 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.\nFamiliarity with pytest, unittest, typing, mypy, pip, poetry, virtualenv, FastAPI, Flask, Django, requests, asyncio, pandas, SQLAlchemy, GitHub, Docker, and CI/CD is preferred.\nExperience leading or supporting remote teams of trainers, engineers, reviewers, annotators, educators, or QAs is strongly preferred.\nComfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.\nHighly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.\nExperience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.\nresponsibilities\nSpot-check Python items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.\nReview AI-generated Python code, debugging responses, algorithms, backend snippets, automation scripts, tests, and explanations.\nUpdate trainers/QAs on Discord about guidelines, workflow updates, and Python-specific quality expectations.\nRespond to questions around Python syntax, runtime behavior, exceptions, package usage, testing, typing, security, performance, and rubric interpretation.\nDM inactive contributors, encourage activation, track follow-ups, and flag availability issues.\nCreate and maintain Python documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.\nRun onboarding/training calls for Python contributors.\nFlag misleading, non-executable, insecure, inefficient, or non-production-ready Python recommendations.\nIdentify recurring quality gaps and improve Python QA workflows.","company":"Sme By Superannotate","rawCompany":"sme by superannotate","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-12T13:27:38.717Z","occupations":[{"code":"15-1253.00","title":"Software Quality Assurance Analysts and Testers","slug":"software-quality-assurance-analysts-and-testers"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"11-3051.01","title":"Quality Control Systems Managers","slug":"quality-control-systems-managers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Python Team Lead","description":"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. 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. 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. 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.\n\nrequirements\nBachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or equivalent professional software engineering experience.\nStrong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.\n3+ years of professional experience in Python development, backend engineering, automation, scripting, data workflows, code review, QA, teaching, or technical mentoring.\nStrong understanding of Python fundamentals such as data structures, functions, classes, modules, exceptions, comprehensions, iterators, generators, decorators, context managers, virtual environments, packaging, and testing.\nAbility 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.\nFamiliarity with pytest, unittest, typing, mypy, pip, poetry, virtualenv, FastAPI, Flask, Django, requests, asyncio, pandas, SQLAlchemy, GitHub, Docker, and CI/CD is preferred.\nExperience leading or supporting remote teams of trainers, engineers, reviewers, annotators, educators, or QAs is strongly preferred.\nComfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.\nHighly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.\nExperience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.\nresponsibilities\nSpot-check Python items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.\nReview AI-generated Python code, debugging responses, algorithms, backend snippets, automation scripts, tests, and explanations.\nUpdate trainers/QAs on Discord about guidelines, workflow updates, and Python-specific quality expectations.\nRespond to questions around Python syntax, runtime behavior, exceptions, package usage, testing, typing, security, performance, and rubric interpretation.\nDM inactive contributors, encourage activation, track follow-ups, and flag availability issues.\nCreate and maintain Python documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.\nRun onboarding/training calls for Python contributors.\nFlag misleading, non-executable, insecure, inefficient, or non-production-ready Python recommendations.\nIdentify recurring quality gaps and improve Python QA workflows.","datePosted":"2026-08-12T13:27:38.717Z","dateModified":"2026-08-12T13:27:38.717Z","hiringOrganization":{"@type":"Organization","name":"Sme By Superannotate","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"73dfc29a389cf85d04a49a20"},"url":"https://jobsearcher.com/jobs/73dfc29a389cf85d04a49a20"}}