计算机视觉科学家 (边缘计算与智能家居)_CR at Bosch Group

Shanghai, Shanghai, China -

Full Time

Start Date

Immediate

Expiry Date

04 May, 26

Salary

0.0

Posted On

03 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Deep Learning, AI Optimization, Embedded Systems, Model Deployment, Sensor Fusion, Signal Processing, Python, C++, Data Analysis, Performance Engineering, Quantization, Pruning, Distillation, Robustness Testing, Ablation Studies

Industry

Software Development

Description

Company Description Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. Job Description About the Team | 关于团队 The Bosch Corporate Research is the central hub for pioneering research and innovation within the Bosch Group. We focus on developing next-generation technologies that shape the future of Bosch’s core business areas, including smart home appliances. Our team works on cutting-edge AI solutions to enhance product intelligence, user experience, and sustainability. We design the "brains" for the next generation of smart home appliances. You will join a dynamic team of AI scientists and engineers dedicated to transforming traditional white goods into intelligent IoT devices. 博世中央研究院是博世集团内进行前沿研究与创新的核心机构。我们致力于开发塑造博世核心业务领域未来的下一代技术，包括智能家电。我们的团队专注于前沿人工智能解决方案，以提升产品智能、用户体验和可持续性。我们致力于为下一代智能家电打造“大脑”。您将加入一个充满活力的 AI 科学家与工程师团队，共同致力于将传统白色家电转化为高度智能化的 IoT 设备。 Key Responsibilities | 主要职责 1) Computer Vision Algorithm R&D | 计算机视觉算法研发 Research, develop, and validate computer vision models for fine-grained recognition in complex real-world conditions. 在复杂真实环境下，研发并验证用于细粒度识别的计算机视觉算法。 Build training and evaluation of pipelines, including data preparation, augmentation, robustness testing, and error analysis. 构建训练与评估流程，包括数据准备、增强策略、鲁棒性测试与误差分析。 2) Edge AI Optimization & Deployment | 端侧 AI 优化与部署 Design lightweight, high-efficiency deep learning models for embedded/edge platforms. 面向嵌入式/端侧平台设计轻量高效的深度学习模型。 Optimize and deploy models using quantization / pruning / distillation, meeting constraints on latency, memory, and power. 通过量化/剪枝/蒸馏等方法优化并部署模型，满足时延、内存、功耗等约束。 Conduct on-device profiling and performance tuning with common inference toolchains. 基于主流推理工具链完成端侧profiling 与性能调优。 3) Multi-sensor Fusion | 多传感器融合 Fuse vision with other sensor modalities (e.g., inertial/vibration, load, spectral) to improve performance and reliability. 融合视觉与其他传感模态（如惯导/振动、载荷、光谱等），提升性能与可靠性。 Design fusion strategies and run ablation studies to quantify sensor contributions and failure modes. 设计融合策略并开展消融分析，量化传感器贡献与失效模式。 4) System Co-design, Innovation & Transfer | 系统共设、创新与转化 Collaborate with hardware and product teams to define camera/optics specifications and overall system architecture. 与硬件及产品团队协作，定义摄像头/光学规格与系统架构。 Scout emerging technologies, generate patents/publications, and transfer research into production-ready solutions. 跟踪前沿技术，产出专利/论文，并推动成果走向可量产方案。 Qualifications Computer Vision & Deep Learning | 计算机视觉与深度学习 Hands-on experience in at least one of: detection / segmentation / fine-grained classification / texture & material recognition. 至少在以下一项有落地经验：检测 / 分割 / 细粒度分类 / 纹理与材质识别。 Proficient in PyTorch or TensorFlow; capable of training, tuning, evaluation, and reproduction. 精通 PyTorch 或 TensorFlow；具备训练、调参、评估与复现能力。 Solid data & experimentation practice: data cleaning, annotation strategy, augmentation, bias/robustness analysis. 具备数据与实验规范：数据清洗、标注策略、数据增强、偏差与鲁棒性分析。 Embedded/Edge AI Deployment & Optimization | 端侧部署与模型优化 Proven deployment experience on at least one platform: ARM / DSP / NPU. 具备至少一种平台的部署经验：ARM / DSP / NPU。 Familiar with model optimization: quantization (PTQ/QAT), pruning, distillation, mixed precision, structural re-parameterization. 熟悉模型优化：量化（PTQ/QAT）、剪枝、蒸馏、混合精度、结构重参数化。 Experience with at least one inference/compile toolchain: TFLite/TFLite Micro, TensorRT, TVM, ONNX Runtime, vendor NPU SDK. 熟悉至少一种推理/编译工具链：TFLite/TFLite Micro、TensorRT、TVM、ONNX Runtime、厂商 NPU SDK。 Performance engineering: profiling latency/throughput/memory/power, bottleneck analysis (operator-level preferred). 具备性能工程能力：profiling 延迟/吞吐/内存/功耗，并能定位瓶颈（算子级分析优先）。 Multi-sensor Fusion & Signal Processing | 多传感器融合与信号处理 Experience in sensor fusion: Kalman/Particle filtering or multi-modal neural fusion. 有融合经验：卡尔曼/粒子滤波或多模态神经网络融合。 Signal processing capability for time-series/sensor signals: FFT/STFT, time-frequency analysis, statistical features. 具备时序/传感信号处理能力：FFT/STFT、时频分析、统计特征提取。 Engineering & Implementation | 工程实现能力 Strong coding in Python & C/C++, with maintainable engineering practices (modularization, testing, optimization). 扎实的 Python 与 C/C++ 编程能力，具备可维护工程习惯（模块化、测试、优化）。 Familiar with embedded/edge dev & debugging workflow: cross-compiling, logging/tracing, HIL testing. 熟悉端侧开发与调试流程：交叉编译、日志/追踪、硬件在环测试。 Soft Skills | 通用能力 Strong analytical and problem-solving ability; able to drive end-to-end delivery. 出色的分析与问题拆解能力，能推动端到端交付。 Good communication in both English & Chinese (written and verbal) for global collaboration. 良好的中英文沟通能力（书面与口头），适应全球协作。 Preferred Qualifications (Bonus) | 加分项 Deep expertise in texture analysis / material recognition / fine-grained recognition (industrial scenes preferred). 在纹理分析 / 材质识别 / 细粒度识别方面有深入积累（工业场景优先）。 Multi-modal perception experience: fusion of vision + acoustic/vibration/spectral/IMU signals. 多模态感知经验：融合视觉 + 声学/振动/光谱/惯导等信号。 Optical & illumination design: optical path/lighting design, lens selection, imaging system setup & calibration. 光学与照明设计：光路/照明设计、镜头选型、成像系统搭建与标定。 Spectral analysis: spectral sensors, preprocessing & calibration, feature extraction, quantitative/semi-quantitative modeling. 光谱分析：光谱传感器应用、预处理与校准、特征提取、定量/半定量建模。 Camera imaging pipeline knowledge: ISP tuning / image quality optimization (AE/AWB/NR/HDR, etc.). 摄像头成像链路：ISP 调试 / 画质优化（AE/AWB/降噪/HDR 等）。 Publications or high-quality patents in top venues (CVPR/ICCV/ECCV/NeurIPS, etc.). 顶会/顶刊论文或高质量专利产出（CVPR/ICCV/ECCV/NeurIPS 等）。 Experience in home appliances / smart home product development (e.g., washing machines, refrigerators, dishwashers), from algorithm R&D to productization. 具备家电/智能家居产品研发经验（如洗衣机、冰箱、洗碗机等），有从算法研发到工程落地/产品化的经验。博世集团网上招聘数据处理同意声明个人信息及目的：你的如下个人信息将被处理并用于招聘：姓名、电子邮箱、电话号码、所在地区、经验和教育信息、简历等应聘者自主提交的附件信息、证件号、职位、部门、计划入职日期、劳动合同持续时间、工作地点、月薪、年薪、浮动奖金比例、餐贴、车贴。博世将依据相关的数据保护法规，在全球范围内对您的数据进行保密。招聘流程中，您的个人信息只有在必须时才会被提供给博世以外的第三方机构(如代理机构)。博世精心挑选了第三方机构并与之签订了保密合同。博世采取了种种保密措施以保护您的数据，确保其不会遭受操纵、丢失、破坏、未经授权的访问或泄漏。我们的安全措施将随着新技术的应用而不断升级。您的本地浏览器和我们的人才招募系统之间的数据传输通过https加密。特定职位申请：申请博世集团的职位就意味着您将自己的个人信息提供给博世集团旗下相应的法律实体（具体请见博世集团法人清单）。请注意，若有任何文件或资料变更，请在所有申请书里一并变更。人力资源部门的员工可能与您联系，询问您是否同意将您的求职申请转向博世集团内部另一合适职位。如果您申请了特定职位，那么只有在您同意的情况下，博世才会将您的求职申请转向另一职位。您可以随时撤回求职申请，博世将根据相关法律删除您的个人信息。与您的申请相关的所有资料将被保留至24个月。您的权利：若您希望停止向博世提供您的个人信息，可联系我们或通过博世网上招聘数据隐私声明中的BKMS系统提出申请。您可以在SmartRecruiters平台查阅和修改您的简历。您对您的个人信息的处理享有知情权、决定权，您有权限制或者拒绝我们对您的个人信息进行处理。您还可以更正、补充您的个人信息。跨境传输：为了实现前述目的，我们所收集的您的个人信息可能会在多个国家或地区间进行跨境转移，例如德国、新加坡。德国：罗伯特-博世有限公司（德国格宁根市罗伯特博世广场1号（Robert-Bosch-Platz 1, Gerlingen-Schillerhohe, Germany，邮编：70839），用于集团统一招聘管理（博世中国与数据接收方均基于该目的开展数据出境活动；英国：SmartRecruiters（英国伯克郡温莎亚瑟路圣斯蒂芬大厦邮政区码: SL4 1RU），用于招聘、后期交流联系及分享职位/活动信息、潜在人才社区邀请。您可通过上述联系方式依法向境外接收方行使您在个人信息处理活动中的权利。存储您的个人信息：我们将仅在为实现目的必要的范围内以及法律法规要求的时间内保留您的个人信息。雇佣：被雇佣后，您提供的数据将被从当前招聘系统转移至职位所属公司的人力资源管理系统。联系方式：请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中“9.联系方式” 博世集团法人清单：请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中获取现阶段使用网上招聘系统的博世集团法人（第五页） Legal Entity: Bosch (China) Investment Ltd.

Responsibilities

The role involves researching and developing computer vision algorithms for smart home appliances, optimizing AI models for edge deployment, and collaborating with hardware teams. Additionally, the candidate will work on multi-sensor fusion to enhance product performance and reliability.