Python (ML-Focused) Team Lead

Skills

About this role

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.

Python, Machine Learning, Data Science

Responsibilities

• Quality monitoring: Spot-check Python ML items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• 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.
• Trainer and QA communication: Update contributors on Discord about guideline changes, workflow updates, and Python/ML-specific review standards.
• Question handling: Respond to questions around Python syntax, package usage, data leakage, model validation, metrics, and statistical assumptions.
• Trainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.

Qualifications

• Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Engineering, or a closely related quantitative field.
• Strong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of professional experience in Python development, machine learning, data science, ML engineering, model evaluation, research engineering, technical review, or ML education.
• Strong understanding of Python fundamentals such as data structures, functions, classes, iterators, comprehensions, exception handling, virtual environments, package management, testing, and debugging.
• Strong understanding of ML topics such as supervised/unsupervised learning, feature engineering, train/test splits, cross-validation, model selection, data leakage, regression, classification, and reproducibility.