Remote Java QA Lead: Code Quality & Mentorship

YO IT Consulting; Netherlands; Sur place; EUR 82 656 - 123 984; Plein temps

Résumé du poste

YO IT Consulting is seeking a Java Quality Assurance Lead for a remote, hourly contractor role. You will oversee quality across Java AI training projects, review AI-generated code, evaluate against rubrics, and provide precise feedback to keep contributors aligned with client guidelines.

You will lead QA workflows with distributed teams, ensure code correctness, readability, performance, and security, and help define style guides and onboarding materials for Java contributors.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, IT or equivalent experience.
• Excellent English to follow guidelines and provide clear feedback.
• 3+ years in Java development, backend engineering, or QA with mentoring.
• Strong Java fundamentals and modern features (OOP, streams, concurrency, JVM).
• Experience evaluating content against rubrics and identifying issues.

Responsabilités

• Quality monitoring: spot-check Java items, identify issues, give feedback via DMs, escalate issues.
• Code review: assess AI-generated Java code, Spring/code snippets, and explanations.
• Trainer/QA communication: update Discord with guideline changes and review standards.
• Question handling: answer Java syntax, OOP, collections, streams, concurrency and Spring topics.
• Activation management: DM idle contributors, track follow-ups, flag availability.
• Documentation: create Java style guides, trackers, FAQs, onboarding materials.
• Onboarding and training: run onboarding calls for Java contributors.
• Risk and security review: flag insecure or non-production-ready Java recommendations.
• Process improvement: identify quality gaps and improve QA workflows.

Connaissances

English proficiency

Java programming

Leadership / mentoring

Code review / QA

Attention to detail

Formation

Bachelor’s or Master’s degree in Computer Science / Software Engineering / IT

Outils

Spring Boot

Maven

Gradle

JUnit

Mockito

Hibernate/JPA

REST APIs

JDBC

IntelliJ / Eclipse

Docker

GitHub

CI/CD

Logging / profiling tools

Description du poste

About This Role

In this hourly, remote contractor role, you will work as a Java Quality Assurance Lead 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. 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. This role is a fast-growing AI Data Services company 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. 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.

Job Description

Job Title: Java Quality Assurance Lead

Job Type: Contract

Location: Remote

About This Role

In this hourly, remote contractor role, you will work as a Java Quality Assurance Lead 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. 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. This role is a fast-growing AI Data Services company 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. 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.

Your profile

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or equivalent professional software engineering experience.
• Strong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of professional experience in Java development, backend engineering, enterprise software, JVM systems, code review, software QA, or technical mentoring.
• 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.
• 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.
• 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.
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, coding mentors, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
• Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation.
• Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based code review is a strong plus.
  
  

Key responsibilities

• Quality monitoring: Spot-check Java items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Code review: Evaluate AI-generated Java code, Spring/backend snippets, debugging responses, algorithmic solutions, tests, architecture explanations, and step-by-step reasoning.
• Trainer and QA communication: Update contributors on Discord about guideline changes, workflow updates, and Java-specific review standards.
• Question handling: Respond to questions around Java syntax, OOP design, collections, streams, concurrency, exceptions, Spring Boot, testing, security, and rubric interpretation.
• Trainer/QA activation management: DM inactive contributors, track follow-ups, and flag availability issues.
• Documentation: Create and maintain Java style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding and training: Run onboarding/training calls for Java contributors.
• Risk and security review: Flag insecure, misleading, non-compilable, inefficient, or non-production-ready Java recommendations.
• Process improvement: Identify recurring quality gaps and improve QA workflows.