Python Team Lead

SME Careers; United States; Remote; USD 55,000 - 83,000; Full time

Job summary

Joinkeywork is seeking a Python (Generalist) Quality Assurance Lead (QAL) for an hourly remote contractor role. You will oversee quality across Python AI training projects, review AI-generated code, and provide precise feedback to trainers and QAs to uphold guidelines.

You will assess code correctness, readability, performance, and security, update teams on Python standards, and help onboard new contributors. This role supports a fast-growing AI data services environment.

Qualifications

• Bachelor’s or Master’s degree in a related field or equivalent experience.
• 3+ years of professional Python development, backend engineering, automation, or QA.
• Strong knowledge of Python fundamentals and testing, data structures, decorators, packaging.
• Experience evaluating code against rubrics and identifying issues.
• Familiarity with CI/CD, Docker, GitHub, and remote collaboration tools.

Responsibilities

• Spot-check Python items and provide feedback.
• Review AI-generated Python code and tests against guidelines.
• Update trainers and QAs on Discord about quality expectations.
• Respond to Python syntax, runtime issues, and security questions.
• DM inactive contributors and track follow-ups.
• Create and maintain Python documentation, style guides, onboarding materials.
• Run onboarding/training calls for Python contributors.
• Flag non-executable, insecure, or non-production-ready code.
• Identify recurring quality gaps and improve QA workflows.

Skills

Python development

Remote team collaboration

Technical mentoring

Documentation upkeep

English proficiency

Education

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or equivalent

Tools

pytest

unittest

typing

mypy

pip

poetry

virtualenv

FastAPI

Flask

Django

requests

asyncio

pandas

SQLAlchemy

GitHub

Docker

CI/CD

Job 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.

Responsibilities

• Spot-check Python items, identify quality issues, provide feedback through DMs, and elevate recurring or critical issues.
• Review AI-generated Python code, debugging responses, algorithms, backend snippets, automation scripts, tests, and explanations.
• Update trainers/QAs on Discord about guidelines, workflow updates, and Python-specific quality expectations.
• Respond to questions around Python syntax, runtime behavior, exceptions, package usage, testing, typing, security, performance, and rubric interpretation.
• DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Create and maintain Python documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.
• Run onboarding/training calls for Python contributors.
• Flag misleading, non-executable, insecure, inefficient, or non-production-ready Python recommendations.
• Identify recurring quality gaps and improve Python QA workflows.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Information Technology, or equivalent professional software engineering experience.
• Strong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of professional experience in Python development, backend engineering, automation, scripting, data workflows, code review, QA, teaching, or technical mentoring.
• Strong understanding of Python fundamentals such as data structures, functions, classes, modules, exceptions, comprehensions, iterators, generators, decorators, context managers, virtual environments, packaging, and testing.
• 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.
• Familiarity with pytest, unittest, typing, mypy, pip, poetry, virtualenv, FastAPI, Flask, Django, requests, asyncio, pandas, SQLAlchemy, GitHub, Docker, and CI/CD is preferred.
• Experience leading or supporting remote teams of trainers, engineers, reviewers, annotators, educators, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
• Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.