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Open nowPosted 50 days ago

具身智能仿真引擎架构师

xiaopeng/index1,586 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身智能仿真引擎架构师xiaopeng/index · Shenzhen, Guangdong, Chinese Mainland
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Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. xiaopeng/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 50 days ago

xiaopeng/index median: 1 days open

The posting

职位描述

我们正在建设面向人形机器人的仿真平台,让机器人在可交互的虚拟环境中完成运动、导航、操作与人机交互,并通过可复现的实验验证其能力。平台连接机器人软件、物理世界和模型评测,帮助算法团队发现问题、比较方案,并持续缩小仿真与真实机器人之间的差异。

你将承担仿真平台关键架构与核心模块的设计开发,与机器人算法、软件、硬件团队协作,把复杂的物理与交互问题转化为可验证、可持续演进的平台能力。我们期待你既能做出架构判断,也能深入代码、模型和实验,带领工程师解决影响机器人表现的关键问题。

岗位职责 1. 平台架构与核心开发:主导仿真平台关键架构和核心模块设计,建立机器人、物理引擎、传感器、环境与评测之间的清晰接口,推动平台的稳定性、可扩展性和工程交付。 2. 机器人建模与控制仿真:建设机器人运动学、动力学及控制系统的仿真集成能力,支撑全身运动、导航和操作等算法的闭环验证。 3. 物理真实性与仿真验证:围绕接触、执行器和传感器等影响任务表现的关键因素,建立基于实验的建模、校准与验证方法,提升仿真与真机行为的一致性。 4. 交互环境与评测:协同相关方向建设可交互场景、多智能体及自动评测能力,支持单模块与完整任务验证,完善过程记录、结果分析和问题复现。 5. 技术演进与团队协作:结合任务需求评估物理、渲染和生成式技术,推动关键能力的工程落地;与基础设施团队协作优化运行效率,指导工程师完成设计、开发与技术验证。

任职要求 1. 计算机、机器人、自动化、机械、计算机图形学等相关专业本科及以上学历。 2. 具备 5 年及以上机器人仿真、物理引擎、自动驾驶闭环仿真或相关系统研发经验,有复杂系统架构设计及核心模块落地经历。 3. 精通 C++ 或 Python,具备另一门语言的工程使用能力;具有扎实的软件设计、系统调试和性能分析能力,能够深入解决核心技术问题。 4. 在机器人建模与控制仿真、物理与接触仿真、仿真引擎架构中,至少一个方向有深入积累;理解机器人运动学与动力学基础,能够分析模型、控制与环境对仿真结果的影响。 5. 熟悉至少一种主流机器人仿真平台或物理引擎,有核心模块开发、二次开发或复杂项目应用经验,能够判断其能力边界与工程适用性。 6. 能够结合实验与数据,在物理精度、运行效率和交付需求之间做出合理取舍,并推动跨团队问题解决与工程师协作。

加分项 - 有人形机器人、足式机器人或机械臂项目经验,熟悉运动控制、全身控制、学习型控制或 Sim-to-Real 验证。 - 有视觉语言动作模型(VLA)的应用、部署或仿真评测经验,理解智能操作任务的测试设计与失败分析。 - 在系统辨识、复杂机构、接触求解、柔性体或传感器仿真方面有深入研究或工程成果。 - 有 Isaac Sim、MuJoCo 等平台的深入开发经验,或有游戏引擎、实时渲染、三维重建与生成资产接入物理仿真的经验。 - 有多智能体环境、评测 Agent 或大规模自动化仿真系统建设经验。

欢迎在机器人、物理仿真或引擎平台领域有深厚积累的候选人。我们看重你在关键方向上的技术深度,以及把复杂问题做成可靠系统的能力。

我们期望你具备的特质 1. 系统架构能力:能够统一物理、渲染、计算、数据与评测链路,建立清晰、可演进的仿真平台边界; 2. 工程落地能力:既能完成顶层设计,也能深入核心代码、仿真模型和性能数据,解决复杂工程问题; 3. 技术判断力:能够客观看待仿真精度与现实差距,以数据和实验驱动技术选型与持续优化; 4. 协同推动力:能够与算法、机器人本体、平台软件和数据团队建立共识,推动仿真能力在训练与产品验证中落地。

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