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Open nowPosted 75 days ago

多模态世界模型算法工程师

nwd4iy9rd2s/referral79 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now多模态世界模型算法工程师nwd4iy9rd2s/referral · Beijing, 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. nwd4iy9rd2s/referral 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. 57.8%60 days
This job: posted 75 days ago

nwd4iy9rd2s/referral median: 1 days open

The posting

聚焦下一代具身智能模型与技术范式，重点推进 Omnimodal World Models（全模态世界模型） 方向。我们希望构建能够统一理解和生成语言、视觉、视频、动作、触觉、本体感知等多模态信息的机器人基础模型，使机器人具备更强的物理世界理解、交互预测、任务规划和泛化执行能力。 【核心职责】 1、负责全模态世界模型的架构设计、训练方法与算法迭代，探索语言、视觉、视频、动作、触觉等模态的统一建模。 2、推进世界模型在物理场景理解、未来状态预测、动作生成、交互结果模拟、机器人策略学习等方向的技术攻关。 3、面向从双臂系统、轮式人形到全尺寸人形机器人等多种机器人形态，并进一步探索灵巧手等高自由度系统，构建统一的动作表示与数据体系。 4、参与机器人多模态数据的采集、清洗、训练、评测与闭环迭代，推动模型在真实机器人任务中的验证与落地。 5、持续跟踪多模态大模型、视频生成、具身智能、world model、robot learning 等前沿方向，形成技术预研、论文和核心技术壁垒。

1、计算机、人工智能、机器人、自动化等相关专业硕士及以上学历，博士优先。 2、具备扎实的深度学习和大模型基础，熟练掌握 Python / PyTorch，具备较强的论文复现、模型训练和工程实现能力。 3、熟悉多模态大模型、视频生成、世界模型、VLA、模仿学习、强化学习、机器人学习中的一个或多个方向。 4、理解真实机器人系统中的感知、动作、时序决策和跨场景泛化问题，有机器人相关经验优先。 5、具备优秀的英文文献阅读能力、技术判断力和自驱力，愿意参与高不确定性、高挑战度的前沿技术探索。 【加分项】 1、有 CVPR / ICCV / ECCV / ICRA / IROS / RSS / CoRL / ICML / NeurIPS / ICLR 等顶会论文经验。 2、参与过多模态大模型、视频生成模型、VLA、world model、robot foundation model的研发。 3、有真实机器人、仿真平台、灵巧手、人形机器人、触觉传感器或机器人数据采集经验。 4、有大规模模型训练、多机多卡训练、数据工程、推理部署或开源项目经验。 我们希望你不仅关注当下模型效果，也愿意一起思考：下一代机器人基础模型应该如何理解物理世界、预测交互后果，并在不同机器人形态之间实现可泛化的智能。

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