Python AI Data Training QA Lead - The United States

Role Description

Are you ready to take on a pivotal role in ensuring the highest standards in AI training? We are looking for a Python Quality Assurance Lead to help ensure consistently high standards in our AI data training projects. This role combines the opportunity to work with modern AI systems with the responsibility of maintaining high-quality Python content, making it a strong fit for people who are passionate about both technology and precision. You will play a crucial role in guaranteeing the quality and efficiency of our AI training processes, ultimately contributing to the success of our projects.

Your Profile

1. Academic / professional background: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
2. Python expertise: At least 7+ years of experience in Python, including real-world projects or production systems.
3. Frameworks & APIs: Proficiency with popular Python frameworks such as Django, Flask, or FastAPI, and experience with RESTful API and GraphQL design and implementation.
4. LLM training experience: Hands-on experience with LLM projects, data annotation, or AI training, preferably in QA or lead roles.
5. English proficiency: Minimum C1 English for documentation, async communication, and collaboration with a global team.
6. Analytical excellence: Strong analytical skills and the ability to review complex code and tasks systematically.
7. Attention to detail: Commitment to precision and thoroughness in the training and review process; ability to produce high-quality work with no supervision.
8. Leadership mindset: Comfortable working independently, giving feedback, and keeping the trainer/QA community engaged and supported.

Key Responsibilities

1. Quality: Conduct regular spot checks on Python tasks and code submissions, provide clear and constructive feedback to trainers, and escalate issues when necessary.
2. Communication: Keep trainers and QAs updated on Discord about new items or project changes, and respond to their questions in a timely and professional manner.
3. Activation management: Monitor trainer/QA activity and proactively communicate with inactive contributors to understand blockers and encourage re-engagement.
4. Documentation: Create, maintain, and improve key documents such as coding guidelines, style guides, trackers, FAQs, honeypots, and related documents.
5. Onboarding & training: Schedule, organize, and run onboarding and training calls with trainers/QAs to ensure they understand the guidelines, expectations, and Python quality standards.