薪酬概览
平均月薪
¥7800
中位数 ¥6000 ｜ 区间 ¥6100 - ¥9400
数据来源：样本数量过少，仅供参考；更新时间：2026年7月13日
月薪分布
75% 人群薪酬落在 0-8k
三大影响薪酬的核心维度
影响薪资的核心维度1：工作年限
影响薪资的核心维度2：学历背景
市场需求
6月新增岗位
8
对比上月：岗位减少7
数据由各大平台公开数据统计分析而来，仅供参考。
岗位需求趋势
不同经验岗位需求情况
| 工作年限 | 月度新增职位数 | 职位占比数 |
|---|---|---|
| 应届 | 2 | 28.6% |
| 不限经验 | 5 | 71.4% |
热招职位
负责储能产品制工艺审查，以保证产品正确性、可装配性、一致性、稳定性；
结合IPD流程，在产品生产定型阶段对产品问题关闭率、工艺文件齐套性和准确性进行统计及跟踪；
负责新产品开发配线表编制、线束设计开发及产品测试作业指导书编制 ；
结合国家或行业标准，编制工艺工作相关规范标准；
负责产品新开发物料的检验与验证；
负责产品测试平台及装置的开发；
负责解决产品制造环节及现场运行电气问题导致故障排查与解决；
负责产品委外制造工艺管理，产品制造技术管理，与产品研发及其他部门的沟通。
1、协助区域开展销售工作，实战销售一线业务；
2、协助区域开展消费者营销工作；
3、多维度分析区域数据，协助上级制定销售策略；
4、负责校园招聘项目推广工作。该岗位设置实习期，实习期通过后可转正。
1）负责自动驾驶算法测试体系化建设，建设测试流程和测试手段；2）负责测试策略制定，能够分层做好问题拦截，保障交付质量；3）负责问题优先级和影响的判断，负责业务运营线上问题、跟进以及验证问题解决；4）协助整理行业对标工具/方法的基础调研，提出建设性的建议。
Core Objective: Responsible for the overall management of the warehouse. Translate annual goals into actionable monthly strategies, execute plans, conduct regular performance reviews, and lead the team to achieve core business KPIs.Efficiency Management: Drive refined operations to maximize efficiency. Develop and implement optimization plans to continuously improve on-site performance.Performance & Metric Operations: Monitor daily operational metrics (efficiency, quality, and shrinkage). Utilize data analysis to identify bottlenecks and collaborate with stakeholders to drive improvements.Resource & Cost Management: Oversee monthly procurement of warehouse supplies. Standardize procurement categories and implement cost-saving initiatives through effective price comparison and vendor management.Specialized Analysis: Manage and report on abnormal metrics, providing actionable insights for resolution.Administrative & System Support: Maintain the inventory management system (excess/shortage/damage), manage meeting room scheduling, and oversee warehouse expense control.
1、协助质量负责人搭建、维护和改进质量体系，参与质量体系文件（管理制度、SOP等）的编制、审核、修订与分发工作，确保文件管理规范；
2、负责生产过程、细胞存储环节的质量监督检查，核对生产操作是否符合SOP要求；
3、负责洁净区环境的日常监测；
4、负责质量相关文件、记录的归档与管理，确保记录完整、可追溯；
5、协助质量负责人开展质量培训、质量审核、偏差处理、不合格品管理等工作，整理相关记录与报告。
Core Objective: Translate annual transportation goals into actionable monthly strategies, execute plans, conduct regular performance reviews, and lead the team to achieve core business KPIs.Resource & Vendor Management: Leverage local resources to mobilize vendor support and enhance operational collaboration.Fleet & Distribution Expertise: Possess deep understanding of urban distribution, with proven experience managing fleets of 100+ vehicles daily.Cost & Talent Optimization: Demonstrate expertise in transportation cost reduction, driver management, and professional training.
System QA – DBW/底盘 方向
岗位职责：
参与L2+/L4车型线控/底盘软硬件研发验证；参与DBW/底盘车型线控功能的需求澄清，输出相关测试用例，执行测试并开发自动化测试；参与DBW控制设备控制功能逻辑的需求澄清，输出测试用例，执行测试并开发自动化测试；参与车辆相关SIL/HIL/VIL 需求澄清，输出测试用例，执行测试并开发自动化测试；岗位要求：
本科以上学历，电子、计算机、自动化、车辆等相关专业；掌握CAPL，Python，C/C++等编程语言；熟练掌握Matlab，Simulink软件，熟悉MBD建模思想。对嵌入式有一定了解。对整车线控原理及系统设计了解。熟练掌握CANOE软件，能通过CAPL编写自动化测试用例。了解自动驾驶软件框架及ROS中间件。熟悉ISO 15765，ISO 13400，ISO 14229等车载诊断，功能安全等协议者优先；有SIL，HIL，VIL开发验证经验者优先；较好的团队协作和沟通能力；有较强的主观能动性, 要能发现并解决问题，能适应敏捷开发工作的风格。
【2027届实习】算法测试QA实习生（2027届-可转正）-环卫车事业部-广州
【2027届】算法测试QA实习生（2027届-可转正）-环卫车事业部-广州
工作职责：
1、参与系统测试与验证：协助完成环卫车系统的功能测试、性能验证及场景评估，参与仿真测试与软件在环验证，学习并输出测试分析报告；
2、支持问题分析与优化：在指导下分析仿真及实车测试数据，协助定位系统问题，推动算法迭代优化；
3、参与测试工具开发：学习并使用自动化测试工具，辅助开发脚本提升测试与数据处理效率；
4、辅助标准与流程建设：参与自动驾驶相关评价体系的测试验证，支持系统合规性迭代。
任职要求：
1、本科及以上学历在校生，通信、电子工程、自动化、车辆工程、计算机等相关专业；
2、对智能驾驶、机器人或物流系统感兴趣，有相关项目或实习经验者优先；
3、具备以下一项或多项能力：
熟悉Python/C++等语言，有基础脚本编写能力；
了解Linux操作系统及常用命令，掌握Git基本使用；
对软件测试流程有基本认知，具备逻辑分析和问题定位意识；
具备良好的学习能力和团队协作精神，能快速适应技术环境。
能连续实习6个月以上优先
- Responsible for product experience operations, including establishing product experience mechanism standards, building an experience metrics system, and enhancing experience operation capabilities.
- Monitor data trends and anomalies to proactively identify product quality issues, user experience gaps, or system inefficiencies.
- Establish a data monitoring mechanism, perform daily data extraction, analysis, and visualization using tools such as SQL to identify operational bottlenecks and risk points.
- Build strong collaborative working mechanisms with business teams and establish an effective closed-loop process with internal service teams to jointly improve operational outcomes.
任职资格
- Educational Background: Full-time bachelor's degree or higher.
- Work Experience: Over 3 years of experience in data analysis, data product management, or data engineering. Experience in SQL is needed.
- Professional Skills: Strong understanding of data architecture and quality assurance processes; excellent data mining, analysis, and visualization skills; experience in defining and managing KPIs for AI service or digital products.
- Soft Skills: Excellent communication, execution, and cross-team collaboration skills; strong problem-solving and logical thinking abilities.
- Language Skills: Proficiency in written and spoken English.
- Preferred Qualifications: Project management experience is a plus.
- AI Quality Assessment Framework & Standards
- Establish and maintain quality evaluation criteria for LLMs, covering accuracy, safety, compliance, and instruction-following capabilities.
- Design evaluation workflows combining automation and human-in-the-loop assessment to quantify model performance regularly.
- End-to-End Quality Control & Risk Management
- Own regression testing and acceptance testing for model iterations, enforcing quality gates to prevent performance degradation before launch.
- Monitor online performance to mine for badcases, identifying model hallucinations, logic errors, or experience defects, and issue timely risk alerts.
- Issue-Driven Improvement & Closed-Loop Management
- Lead Root Cause Analysis for quality issues, precisely diagnosing whether failures stem from data, prompts, or model architecture.
- Drive algorithm, product, and operations teams to resolve quality defects, track the fix rate and conduct re-validation to ensure a closed-loop process.
- Cross-functional Collaboration
- Bridge the gap between business needs and technical implementation, ensuring the AI delivers real value to users.
任职资格
- Educational Background: Full-time bachelor's degree or higher.
- Work Experience: 3+ years of experience in internet product operations. Hands-on experience in LLM/AIGC product operations is highly preferred.
- Professional Skills:
- Mastery of AI evaluation methodologies (e.g., Golden Dataset construction, Human Evaluation SOPs).
- Sharp eye for detail ("bug hunting" mindset) with the ability to mine potential risks from massive data.
- Familiarity with Prompt Engineering to reproduce or verify issues (focus on validation rather than daily maintenance).
- Soft Skills: Strong cross-functional communication skills; ability to use data to influence and drive cross-functional partners to solve complex problems.
- Language Skills: Proficiency in written and spoken English.
- Preferred Qualifications: Project management experience is a plus.