滴滴Robotaxi Service-物流实习生
任职要求
大学本科或以上学历，物流、供应链管理专业，熟练使用办公软件（如…工作职责
工作职责： 1、负责无人车广州区域的资产出入库、领用、调拨、贴标签等实物管理工作，相关信息及时同步资产系统，保证实物流和信息流一致、做到日清日结、账实相符。 2、负责跨区域资产转移的快递的收发工作，并及时同步跟进快递单号以及物流信息。 3、定期对区域资产进行盘点，及时修正盘点中出现的问题。 4、负责库房的卫生管理、物料摆放，对库房进行目视化以及5S管理； 5、协助上级拓展物流管理纵深，持续优化资产物流、仓储、包装等业务，提高物流效率、降低物流成本。
1. 协助国际业务部门进行海外市场调研、行业趋势分析及竞品扫描，支持战略决策； 2. 参与制定并跟进国际业务拓展计划，协助项目执行与跨部门沟通； 3. 收集并整理国际合作伙伴信息，评估其商业价值与资源匹配度； 4. 支持战略投资、合作项目的尽职调查、数据分析与报告撰写； 5. 撰写会议纪要、项目报告； 6. 协助完成上级交办的其他事务性工作。
1、Robotaxi Service测试开发工程师，负责Robotaxi业务测试与质量体系建设 2、负责订单，交易系统，车辆运营运维中台业务测试 3、负责应用的功能、性能（如响应时间、CPU/内存/FPS等）、稳定性等测试工作 4、熟悉Android ADB、Linux等命令，熟悉车机APP测试（桌面、系统架构）5、根据项目计划，制定测试计划、方案、用例设计及执行测试，输出测试报告，把控业务形态与功能风险 6、深入了解业务，沉淀通用测试解决方案，逐渐负责贴合业务特性的关键技术专项建设 7、提升测试效率与质量覆盖，构建匹配的质量体系，提升产品体验。
THE ROLE As a key member of the Autopilot AI Tooling & Teleoperation team, you will work on two critical areas: Designing and building tooling systems that support the full machine learning lifecycle (data processing, annotation, visualization, training, and productionization pipelines); Developing high-reliability, low-latency backend infrastructure to enable remote operation and assistance for Tesla’s global Robotaxi fleet and Optimus humanoid robots. You will collaborate closely with world-class AI researchers, autonomy teams, firmware, and controls engineers to build an end-to-end technology stack — from data to cloud, and from model training to real-time teleoperation. Your work will directly impact the safety and continuous improvement of thousands of autonomous vehicles and robots. RESPONSIBILITIES Design and implement large-scale data processing pipelines handling diverse data types including autonomous driving images, sensor data, human annotations, and teleoperation video/telemetry/control signals. Build tools, metrics, dashboards, and automation platforms that accelerate the full cycle of model training, validation, and teleoperation workflows. Design and develop scalable, high-performance backend infrastructure to support low-latency remote operation of thousands of vehicles and robots worldwide, with strong emphasis on safety and reliability. Establish comprehensive observability systems (monitoring, metrics, and alerting) to achieve full visibility from edge devices to the cloud. Collaborate with AI researchers, frontend engineers, firmware, and autonomy teams to rapidly turn research ideas into production-grade features. Select and implement cloud-native technologies (Kubernetes, Terraform, etc.), build automated testing frameworks, and enable safe continuous deployment.