{"html":"<p>In this hourly, remote contractor role, you will work as a Python (ML-Focused) Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Python machine learning AI training projects. You will review AI-generated Python code, ML workflows, model explanations, 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, machine learning methodology, statistical validity, reproducibility, model-evaluation quality, data leakage risks, package usage, debugging accuracy, readability, maintainability, formatting, instruction-following, and adherence to project-specific rubrics. This role requires strong Python and ML expertise, English communication skills, excellent attention to detail, and the ability to manage quality workflows across remote technical teams.</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 ML quality leadership will help ensure training data is accurate, executable, statistically sound, reproducible, clearly explained, 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>Quality monitoring: Spot-check Python ML items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.</li><li>Code and ML review: Evaluate AI-generated Python code, ML pipelines, data-preprocessing steps, model training workflows, evaluation logic, debugging responses, and explanations for correctness and reproducibility.</li><li>Trainer and QA communication: Update contributors on Discord about guideline changes, workflow updates, and Python/ML-specific review standards.</li><li>Question handling: Respond to questions around Python syntax, package usage, data leakage, model validation, metrics, statistical assumptions, reproducibility, notebooks, and rubric interpretation.</li><li>Trainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.</li><li>Documentation: Create and maintain Python ML style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.</li><li>Onboarding and training: Run onboarding/training calls for Python ML contributors.</li><li>Risk review: Flag misleading, overconfident, statistically invalid, non-reproducible, insecure, or non-production-ready Python ML recommendations.</li><li>Process improvement: Identify recurring quality gaps and build scalable QA processes.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Engineering, or a closely related quantitative field.</li><li>Strong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback.</li><li>3+ years of professional experience in Python development, machine learning, data science, ML engineering, model evaluation, research engineering, technical review, or ML education.</li><li>Strong understanding of Python fundamentals such as data structures, functions, classes, iterators, comprehensions, exception handling, virtual environments, package management, testing, and debugging.</li><li>Strong understanding of ML topics such as supervised/unsupervised learning, feature engineering, train/test splits, cross-validation, model selection, data leakage, regression, classification, clustering, metrics, bias/variance, regularization, and reproducibility.</li><li>Ability to evaluate ML content against detailed rubrics and identify issues such as flawed methodology, wrong metrics, data leakage, non-reproducible code, invalid assumptions, hallucinated APIs, misleading conclusions, or incomplete explanations.</li><li>Familiarity with NumPy, pandas, scikit-learn, PyTorch, TensorFlow/Keras, XGBoost/LightGBM, Jupyter, matplotlib, seaborn, MLflow, Hugging Face, SQL, GitHub, Docker, and CI/CD is preferred.</li><li>Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, data scientists, ML researchers, coding mentors, or QAs is strongly preferred.</li><li>Comfortable working in fast-moving remote environments using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.</li><li>Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation.</li><li>Experience with AI training, data annotation, LLM evaluation, code QA, ML QA, or rubric-based technical review is a strong plus.</li></ul>"}