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ClosedPosted 45 days ago

导航算法工程师/研究员

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland

This posting closed on 2026-10-23. 86 other wcn7iq2vq1am/863711 postings are live.

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  1. 采购经理3d agowcn7iq2vq1am/863711 · Shanghai, Chinese Mainland
  2. PMO3d agowcn7iq2vq1am/863711 · Shanghai, Chinese Mainland
  3. 机器人运控算法工程师4d agowcn7iq2vq1am/863711 · Shanghai, Chinese Mainland
  4. 机器人应用开发工程师4d agowcn7iq2vq1am/863711 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. wcn7iq2vq1am/863711 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 45 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

岗位定位 导航正在从"几何问题"变成"理解问题"：机器人要知道"这里是哪"（place recognition）、"眼前的场景跟记忆里哪个对得上"（3D scene matching）、"桌子在哪、杯子在哪个台面"（scene understanding、scene graph representation）、"怎么按这句话走过去"（VLN），甚至自己拆解任务、决定下一步（agentic navigation）。我们正在寻找一位导航算法工程师/研究员，核心方向是 learning-based 导航——研究、训练并把这些能力在真机上做出来，让导航从固定管线里的规划，变成建立在场景理解之上的智能行为。

岗位职责 1. 研究场景理解与空间记忆方法——place recognition、3D scene matching、scene graph representation 等，让机器人认得来过的场景、找得到见过的物体，支撑重定位、语义建图与长程空间推理； 2. 研究 VLN 与任务驱动导航——基于自然语言指令在真实环境中导航，集成已有感知与语言模型（VLM/LLM），实现"要去哪、做什么"到行动的闭环； 3. 研究 agentic navigation——让机器人自主拆解导航任务、规划多步行动、调用技能与工具（探索、重试、改道、失败恢复），经典规划栈可作为被组合调用的技能层； 4. 训练与迭代导航策略（end-to-end / RL / 模仿学习），负责 sim2real 迁移（domain randomization、感知对齐等）与真机验证； 5. 构建数据与评测体系——仿真环境与训练管线（Isaac Sim、Habitat 等）、真机数据采集、benchmark 与指标设计，支撑方法快速迭代； 6. 在真机上端到端验证与迭代，与 SLAM/定位、感知、移动操作、仿真团队协作，推动 learning-based 导航能力落地。

1. 机器人、计算机、自动化或相关专业硕士及以上学历，博士优先； 2. 在以下至少一个方向有深入理解和实际经验：place recognition / 3D scene matching / scene graph representation / scene understanding / VLN / agentic navigation； 3. 深入理解视觉与多模态深度学习——表征学习、matching、检测分割、多模态预训练（VLM/LLM）等，有模型设计与训练的实际经验； 4. 有 VLM/LLM 在机器人或导航任务上的研究或使用经验——prompt、微调、agent 框架、工具调用等； 5. 熟悉主流仿真器与具身导航基准（Isaac Sim、Habitat、ManiSkill 等），能独立搭建仿真环境与训练管线； 6. 理解 sim2real 核心挑战，有导航策略或相关模型的真机部署与验证经验者优先； 7. 具备扎实的 Python 工程能力，能使用 C++ 进行系统级开发； 8. 兼具研究深度与工程落地能力——能跟前沿论文（能复现、能超越），更能让真机动起来。

加分项 1. 发表过 place recognition、scene understanding、VLN、embodied navigation 相关顶会论文（CVPR、ICCV、ECCV、NeurIPS、RSS、CoRL、ICRA、IROS 等）； 2. 有 3D scene graph、visual grounding、open-vocabulary detection 的研究或工程经验； 3. 有 VLM/LLM 驱动的 agentic 系统或 VLA 模型的研究与落地经验； 4. 有导航策略（RL / end-to-end / 模仿学习）在真机上的完整落地经验； 5. 参与过具身导航相关 benchmark 建设或开源项目。

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