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We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here - we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.\n高级软件工程师（自动驾驶数据闭环与AI智能故障诊断平台）\n岗位职责\n负责车端故障事件检测与数据自动切片、回传工具开发，支持感知、定位、规划、控制等节点故障信号触发。\n建设企业级自动驾驶数据闭环平台，统一管理多矿山、多车辆、多版本回传数据，并打通数据标注平台。\n负责基于 VLM 的自动化问题诊断能力开发，将图像、点云、轨迹、日志、故障码等多模态信息组织为模型上下文，自动输出故障现象、根因分析与排查建议。\n结合 RAG、工具调用或 Agent 工作流完成复杂诊断，并建立证据引用与人工确认机制以控制模型幻觉。\n负责 VLM 领域微调与评估体系建设（SFT、LoRA/QLoRA），持续沉淀现场数据实现闭环迭代。\n建设 MLOps Pipeline，支持感知、融合等模型的一键训练、验证、导出（ONNX/TensorRT）与车端部署。\n负责平台架构设计与核心开发，保障系统可扩展、高可用、可观测。\n任职要求-基本要求\n计算机、软件工程、人工智能、自动化等相关专业本科及以上学历。\n5年以上软件平台、自动驾驶数据平台或 AI 工程系统开发经验。\n熟练掌握 Python，并掌握 C++、Go 或 Java 之一。\n熟悉 Web 后端、RESTful API、数据库设计及 Docker、Linux、CI/CD。\nAI / VLM方向\n具备 VLM 或多模态大模型实际项目经验，能独立完成从方案到部署的全过程。\n熟悉 SFT、LoRA、QLoRA 等微调方法，能构建多模态训练与评估数据集。\n熟悉 Prompt Engineering、RAG、向量检索、工具调用或 Agent 工作流。\n具备 VLM 推理部署与优化经验（量化、显存优化、推理加速）。\n平台工程方向\n熟悉 React/Vue/TypeScript，能推动复杂数据可视化落地。\n熟悉数据库、对象存储、缓存与消息队列等基础设施。\n熟悉机器学习/深度学习模型的训练、评估与部署流程。\n加分项\n自动驾驶、机器人、ADAS 或矿山自动化项目经验。\n自动驾驶故障诊断、数据回灌、Corner Case 挖掘经验。\n熟悉 ROS/ROS2、ROS Bag、MCAP 等数据格式与通信框架。\n熟悉 LLaVA、Qwen-VL、InternVL、CLIP 等开源多模态模型。\n熟悉 Kubernetes、GPU 调度、vLLM、Triton 或 MLflow/Kubeflow 等工具。\nPosting Dates:\nSeptember 18, 2026 - October 8, 2026Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to apply\nNot ready to apply? 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