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Open nowPosted 256 days ago

算法工程师 （数据生成 / 物理仿真方向）

xtool/index90 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now算法工程师 （数据生成 / 物理仿真方向）xtool/index · Shenzhen, Guangdong, 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. xtool/index 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 256 days ago

xtool/index median: 1 days open

The posting

1. 负责面向工业视觉、智能制造、机器人及激光加工等垂直业务场景的高质量合成数据生成算法研发，重点基于物理仿真、三维渲染、神经渲染及 3D Gaussian Splatting 等技术，为视觉模型、多模态模型及具身智能模型训练提供高可信度数据支撑。 2. 基于数字孪生与仿真引擎，构建贴近真实业务场景的虚拟环境，包括三维几何建模、材质建模、光照配置、相机参数模拟、运动轨迹设计、物体交互与场景动态变化等，实现可控、可扩展、可自动化的数据生成流程。 3. 研究并实现基于物理规律的仿真数据生成方法，围绕光照传播、反射、折射、散射、吸收、阴影、透明/半透明材质、金属/非金属材质、复杂表面纹理等视觉效果进行建模，提升合成图像与视频数据的真实感和可用性。 4. 探索并落地 3D Gaussian Splatting、NeRF、可微渲染、神经渲染、三维重建与生成式 3D 技术 在数据生成中的应用，实现从真实场景重建、可编辑三维表示到多视角、多光照、多材质、多姿态数据自动生成的完整技术链路。 5. 负责合成图像、视频及多模态标注数据的自动化生成，包括目标检测框、实例/语义分割、关键点、深度图、法向图、位姿、光流、三维点云、相机参数及场景元数据等，为下游模型训练提供高质量结构化标注。 6. 结合真实业务数据分布，对仿真数据进行质量评估、域随机化、风格迁移、真实感增强与 Sim2Real 优化，持续提升合成数据在目标检测、图像分割、三维重建、多模态理解等任务中的训练收益。 7. 建立数据生成与模型训练之间的闭环机制，基于模型训练效果、误检漏检案例及真实业务反馈，反向优化仿真场景、材质参数、光照条件、数据分布与生成策略，提升数据生成系统的业务价值。 8. 跟踪物理仿真、神经渲染、3D Gaussian Splatting、三维生成、可微渲染、数字孪生、合成数据生成等前沿技术，推动其在视觉检测、智能制造、机器人感知及激光加工等场景中的工程化落地。

1. 具备扎实的数学、物理、计算机图形学或计算机视觉基础，熟悉线性代数、三维几何、相机模型、渲染管线、光照模型、PBR 材质及基本物理仿真原理。 2. 熟悉至少一种三维仿真、渲染或数字孪生工具/框架，如 Blender、Unity、Unreal Engine、NVIDIA Omniverse、Isaac Sim等。 3. 熟悉或了解 3D Gaussian Splatting、NeRF、神经渲染、三维重建、可微渲染、体渲染、路径追踪、光线追踪、BRDF/BSDF、全局光照 等相关技术，有实际项目经验者优先。 4. 熟练掌握 Python，具备良好的算法实现与工程开发能力；熟悉 C++、CUDA、OpenGL、Vulkan、Blender Python API、Unity/Unreal 自动化脚本或图形学相关开发经验者优先。 5. 熟悉 PyTorch、TensorFlow 等深度学习框架，了解视觉模型训练流程，具备目标检测、图像分割、三维视觉、多模态模型、生成式 AI 或数据合成项目经验者优先。 6. 具备合成数据生成、仿真数据集构建、自动标注、Domain Randomization、Sim2Real、真实感增强、三维场景重建或可控数据生成相关经验者优先。 7. 具备较强的技术调研能力、问题分析能力和工程落地能力，能够独立推进算法调研、原型验证、实验设计、效果评估与迭代优化。

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