特斯拉嵌入式开发实习生,车身控制 Embedded Software Intern, Body Controls
任职要求
What You’ll Bring
BS or MS in Mechatronics, Electrical Engineering, Computer Engineering, Computer Science, experience with evidence of exceptional ability, or equivalent
Proficiency in C with experience of RTOS and motor control
Fluent in software f…工作职责
到岗时间要求:2026年4月或5月 实习时间要求:一周五天全勤,至少3个月,6个月及以上优先考虑。 毕业时间:2026年 转正机会:在表现优异且通过面试考核的前提下可转正 英文:听说读写流利加分,需与美国团队日常会议沟通 What to Expect The Body Controls Firmware Team is responsible for firmware, sensing, and controls of mechatronics systems in Tesla vehicles. The team is responsible for firmware and controls of falcon wing doors, seats, liftgates, windows, latches, wipers, mirrors, various sensing systems, interior and exterior lighting, and control of other small motors in the vehicle. The body controls firmware team is small, passionate, and fast moving. Come join a team of deeply knowledgeable engineers that strive to build the most robust and reliable embedded systems using cutting edge software development tools and practices. What You’ll Do Deliver high-quality C code in a real-time embedded environment Specify, design, and implement functionality and behaviors of embedded subsystems Design the software architecture and firmware implementation Own firmware through life cycle from prototype to high-volume production Develop software tests and collaborate with validation teams Hands-on hardware bring-up, system debugging and code optimization Make performance and optimization trade-offs to meet product
AI搜索和智能体产品后端系统研发: 1. 设计并实现AI搜索Agent应用,包括Query理解、记忆存储、环境感知等模块的集成与优化。 2. 负责Agentic Search(搜索智能体)技术探索和架构研发,支持多模态(文本、图像、视频)检索与应用创新。 3. 抽象并开发企业级别的AI应用平台,支持Agent相关应用的接入与扩展,确保平台的高可用性和可扩展性。 4. 实现平台的模块化设计,支持快速迭代与功能扩展,满足AI时代本地生活服务领域智能体应用快速发展需求。 5. 与业务部门(如产品、运营团队)协作,将AI搜索能力嵌入现有工作流(如智能问答、个性化推荐)。 6. 负责AI系统的日常运维,包括异常监控、接口优化及用户培训,确保生产环境高效运行。
1、嵌入式AI系统开发: • 负责RTOS系统平台上多模态AI终端产品的研发,包括方案评估、软件架构设计、核心功能模块(如人脸/手势识别、行为分析)开发与部署; • 主导端侧AI模型轻量化、跨平台推理框架适配(TensorFlow Lite/MNN/NCNN)及NPU芯片的性能优化(如内存、功耗、实时性); • 结合硬件特性设计轻量化模型架构,完成从算法训练到嵌入式端侧部署的全链路开发。 2、多模态算法工程化: • 优化计算机视觉算法在嵌入式设备(IoT/AR硬件/AI机器人)的落地效果,解决低算力、高延迟、多干扰场景下的工程挑战; • 开发芯片算子库适配方案,参与芯片选型、AI工具链优化及端云协同架构设计; • 探索多模态交互(视觉+语音+传感器)在智能终端的创新应用,如AI玩偶、陪伴机器人等。 3、跨团队协作与交付: • 与芯片厂商、算法团队、硬件团队协同开发,主导端侧SDK集成及性能调优,确保产品按时交付; • 支持产品量产落地,保障系统稳定性与用户体验。