Description

- 搭建并维护用于端到端和 VLA 自动驾驶模型的强化学习闭环训练流程。

- 设计和实现支持 RL 闭环训练与评测的仿真环境。

- 开发高效可扩展的工具链，包括数据管理、实验调度和性能监控。

- 对强化学习算法进行优化，提升训练效率、可扩展性及实时部署能力。

- 与研究团队协作，将新的 RL 方法集成到闭环系统中。

- 记录开发流程与基准结果，提供部署相关的技术支持。

- Build and maintain closed-loop reinforcement learning training pipelines for E2E and VLA autonomous driving models.

- Design and implement simulation environments to support RL-based closed-loop training and evaluation.

- Develop scalable toolchains for dataset management, experiment orchestration, and performance monitoring.

- Optimize RL algorithms for efficiency, scalability, and real-time deployment.

- Collaborate with research teams to integrate new RL methods into the closed-loop system.

- Document development workflows, benchmark results, and provide technical support for deployment.

Qualifications

1.计算机、机器学习、自动化、机器人等相关专业硕士或博士学历。

2. 具备强化学习、仿真环境、大规模训练流程等相关经验。

3. 熟悉自动驾驶仿真平台（如 CARLA、LGSVL、SUMO, GPUDrive, Waymax）或机器人仿真环境。

4. 具备扎实的软件工程能力，精通 Python/C++，有分布式训练与工具链开发经验。

5. 熟悉容器化技术（Docker、Kubernetes）及实验管理工具。

6. 具备良好的问题解决能力和团队协作精神，自驱动。

7. 具备良好的英文读写能力。

1. Master’s/Ph.D. degree in Computer Science, Software Engineering, or related fields.

2. Solid background in reinforcement learning, simulation environments, and large-scale training pipelines.

3. Hands-on experience with autonomous driving simulators (e.g., CARLA, LGSVL, SUMO, GPUDrive, WayMax) or robotics simulators.

4. Strong software engineering skills in Python/C++; experience in distributed training and toolchain development.

5. Familiarity with containerization (Docker, Kubernetes) and experiment management tools.

6. Good problem-solving skills, self-driven, and team-oriented.

7. English reading/writing proficiency.

Additional Info

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.