Java AI Data Training Tech Quality 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 Java Tech 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 Java 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. Java expertise: At least 7+ years of professional experience in Java, with a focus on code quality, code review, or technical evaluations.
3. Technical expertise: Strong expertise in Java programming, debugging, understanding different design and implementation approaches, and assessing code for robustness, readability, and maintainability.
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 & problem-solving skills: Exceptional analytical and problem-solving skills, with the ability to identify root causes and evaluate different solution options.
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. Code quality review: Conduct regular reviews of Java solutions and tasks submitted by trainers, checking for correctness, clarity, code style, structure, and maintainability; provide clear and constructive feedback, and escalate critical issues when necessary.
2. Technical evaluation: Assess Java code for algorithmic soundness, performance implications, and best practices (error handling, modularity, readability), and ensure alignment with project guidelines and standards.
3. Communication: Keep trainers and QAs updated on Discord about new items, review expectations, or project changes, and respond to their questions in a timely and professional manner.
4. Activation management: Monitor trainer/QA activity and proactively communicate with inactive contributors to understand blockers and encourage re-engagement.
5. Documentation: Create, maintain, and improve key documents such as Java coding guidelines, review checklists, example solutions, trackers, FAQs, honeypots, and related documents.
6. Onboarding & training: Schedule, organize, and run onboarding and training calls with trainers/QAs to walk them through Java quality standards, common issues, and review expectations.