{"html":"<p>In this hourly, remote contractor role, you will work as a Java Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Java AI training projects. You will review AI-generated Java code 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, compile-time validity, runtime behavior, object-oriented design, API usage, debugging accuracy, readability, maintainability, performance, security, test coverage, formatting, instruction-following, and adherence to project-specific rubrics. This role requires strong Java 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 Java quality leadership will help ensure Java training data is accurate, executable, idiomatic, secure, 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 Java items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.</li><li>Code review: Evaluate AI-generated Java code, Spring/backend snippets, debugging responses, algorithmic solutions, tests, architecture explanations, and step-by-step reasoning.</li><li>Trainer and QA communication: Update contributors on Discord about guideline changes, workflow updates, and Java-specific review standards.</li><li>Question handling: Respond to questions around Java syntax, OOP design, collections, streams, concurrency, exceptions, Spring Boot, testing, security, and rubric interpretation.</li><li>Trainer/QA activation management: DM inactive contributors, track follow-ups, and flag availability issues.</li><li>Documentation: Create and maintain Java style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.</li><li>Onboarding and training: Run onboarding/training calls for Java contributors.</li><li>Risk and security review: Flag insecure, misleading, non-compilable, inefficient, or non-production-ready Java recommendations.</li><li>Process improvement: Identify recurring quality gaps and improve QA workflows.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or equivalent professional software engineering experience.</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 Java development, backend engineering, enterprise software, JVM systems, code review, software QA, or technical mentoring.</li><li>Strong understanding of Java fundamentals such as OOP, collections, generics, streams, lambdas, exceptions, concurrency, annotations, JVM behavior, memory management, modules/packages, and modern Java features.</li><li>Ability to evaluate Java content against detailed rubrics and identify issues such as non-compilable code, incorrect logic, poor exception handling, unsafe concurrency, weak object modeling, inefficient algorithms, hallucinated APIs, or incomplete explanations.</li><li>Familiarity with common Java ecosystems and tools such as Spring Boot, Maven, Gradle, JUnit, Mockito, Hibernate/JPA, REST APIs, JDBC, IntelliJ/Eclipse, Docker, GitHub, CI/CD, logging, and profiling tools is preferred.</li><li>Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, 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, or rubric-based code review is a strong plus.</li></ul>"}