JMP_AI Vehicle Motion Control Algorithm Expert（VM）

Location: Suzhou, Jiangsu, cn

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

Responsibilities:

1.AI solution design (e.g., Apply reinforcement learning and learning-based control methods under vehicle dynamics and real-time constraints.)

2.Collaborate with system, calibration, and software teams to support integration, tuning, and vehicle testing.

3.Support the transition from prototype algorithms to scalable, SOP-ready solutions.

岗位职责:

1.人工智能解决方案设计(例如：在车辆动力学特性与实时性约束条件下，应用强化学习及基于学习的控制方法。)

2.与系统、标定及软件团队协作，为方案集成、参数调校及实车测试提供技术支持。

3. 助力算法原型向可扩展、满足量产交付（SOP）标准的解决方案转化落地。

Qualifications

Qualifications:

1.Master’s degree or higher in Artificial Intelligence, Robotics, Computer Science, Vehicle Engineering, Control Engineering or a related field.

2.2 years of experience in AI/Robotics/ADAS related industries.

3.Strong background in ML, RS, GenAI.

4.Hands-on experience with machine learning or reinforcement learning (e.g., PPO, SAC, DDPG).

5.Proficiency in Python and/or C++, with experience using PyTorch or Jax

任职要求：

1.人工智能、机器人学、计算机科学、车辆工程、控制工程或相关专业硕士及以上学历。

2.具备 2 年人工智能（AI）/ 机器人学 / 高级驾驶辅助系统（ADAS） 相关行业工作经验。

3.拥有扎实的机器学习、RS、生成式人工智能（GenAI）专业背景。

4.具备机器学习或强化学习领域的实操经验（例如近端策略优化 PPO、软演员评论家算法 SAC、深度确定性策略梯度算法 DDPG）。

5.熟练掌握 Python 和 / 或 C++ 编程语言，且具备 PyTorch 或 Jax 框架的使用经验。

Additional Information

With the rapid evolution of intelligent and electrified vehicles, to enable more adaptive, robust, and data-driven vehicle dynamics control, we are advancing the integration of AI-based control algorithms into production-oriented chassis systems.This position is part of a strategic initiative to develop learning-based vehicle control solutions for real-time on-vehicle deployment. The role focuses on applying machine learning and reinforcement learning techniques to the chassis control functions.The successful candidate will transform domain knowledge and physical models into scalable, production-ready AI algorithms, contributing directly to the next generation of intelligent chassis systems.

随着汽车智能化与电动化的快速发展，为实现更具适应性、稳定性且数据驱动的车辆动力学控制，我们正致力于将基于人工智能（AI）的控制算法，整合至面向量产的底盘系统中。该岗位隶属于一项战略项目，核心任务是研发基于学习的车辆控制解决方案，以实现车载系统的实时部署。岗位工作重心为将机器学习与强化学习技术，应用于底盘控制功能的开发。合格候选人需能够将领域专业知识与物理模型，转化为可扩展、可量产落地的人工智能算法，直接助力下一代智能底盘系统的研发进程。