QA Engineer
** 薪水：** 每小时63美元至120美元。职位标题：QA 工程师角色类型：承包商地点：远程微1正在聘请 QA 工程师与前客户一起为 AI 培训项目做出贡献。在这个角色中，你将应用自己的专业知识来帮助培养下一代人工智能系统。你的工作将塑造模型如何通过高质量的现实输入学习，推理和执行方式。不需要先前的 AI 经验。你的领域知识是重要的。这种机会对于那些热衷于质量保证并渴望在 AI 创新的最前沿的人来说非常理想，重点是数据完整性，注释和全面模型评估。** 工作范围** 1. 设计，执行和记录测试案例和场景以验证 AI 输出与定义要求。2.审查，注释和标签数据集以提高机器学习模型训练精度。3.评估，分析和报告模型预测，识别不一致性，错误或模式进行持续改进。4. 与跨功能团队合作沟通发现，提出问题，澄清项目需求。5. 贡献开发和完善针对人工智能驱动系统的测试方法。6.通过书面文档保持详细，有组织的评价，测试结果和反记录。7. 提供可行的建议，以优化数据质量和模型性能。**优先资格** 1.实践知识在软件质量保证方面，有着对测试规划和故障报告的强烈了解。2.与数据标签，注释，从人体反 (RLHF) 中得到增强学习或模型评估的前经验。3.具有 B2级以上的英语能力，以便有效合作和报告。
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**Pay:** $63-$120 per hour (USD).
Role Title: QA Engineer
Role Type: Contractor
Location: Remote
micro1 is engaging QA Engineers to contribute to an AI training project with a forward-thinking customer. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This opportunity is ideal for those passionate about quality assurance and eager to be at the forefront of AI innovation, with a focus on data integrity, annotation, and comprehensive model evaluation.
**Scope of Work**
1. Design, execute, and document test cases and scenarios to validate AI outputs against defined requirements.
2. Review, annotate, and label data sets to enhance machine learning model training accuracy.
3. Evaluate, analyze, and report on model predictions, identifying inconsistencies, errors, or patterns for continuous improvement.
4. Collaborate with cross-functional teams to communicate findings, raise questions, and clarify project requirements.
5. Contribute to the development and refinement of testing methodologies specific to AI-driven systems.
6. Maintain detailed, organized records of evaluations, test results, and feedback through written documentation.
7. Provide actionable recommendations to optimize data quality and model performance.
**Preferred Qualifications**
1. Hands-on expertise in software quality assurance, with a strong understanding of test planning and defect reporting.
2. Prior experience with data labeling, annotation, reinforcement learning from human feedback (RLHF), or model evaluation.
3. Proficiency in English at B2 level or above for effective collaboration and reporting.