西门子西门子中国研究院 大模型强化学习研究员（上海、北京、苏州）

任职要求

• Master's or Doctor degree or above in Computer Science, Automation, Mathematics or related.
 • Self-motivated, good communication skills and good team player.
 • Ability to handle multiple competing priorities in a fast-paced environment.
 The skills you are expected to have: 
 • 1~3 years of hands-on RL experience (academic or industry).
 • Expertise in deep RL algorithms (model-based/model-free) and frameworks (e.g., RLlib, Gymnasium).
 • Strong Python skills with PyTorch/TensorFlow and proficiency in Linux.
 • Experience with distributed training (Horovod, DeepSpeed) and cloud platforms (AWS/Azure/Alicloud).
 • Familiarity with LLM agents or LLM post-training.
 • Prefer: Backgroun…

工作职责

We empower our people to stay resilient and relevant in a constantly changing world. We're looking for people who are always searching for creative ways to grow and learn. People who want to make a real impact, now and in the future. Does that sound like you? Then it seems like you'd make a great addition to our vibrant international team. DAI AIX – AI Acceleration and Exploration, is working on the cutting-edge research of Data Analytics and AI with Siemens global technology network, and consulting, co-creation, data driven applications for the end customers. Research Scientist is to do applied research for Industrial AI applications in the team. We are seeking a Reinforcement Learning (RL) Specialist to lead the design, implementation, and optimization of RL-driven systems for post-training of foundation models. The primary focus of this role is advancing our RL capabilities for real-world applications such as industrial control systems and LLM agents. You will develop cutting-edge algorithms, improve post-training efficiency, and deploy scalable RL solutions in industry. 
 You'll make an impact by 
 • 1. Reinforcement learning development for post-training: • Design and implement state-of-the-art RL algorithms (e.g., PPO, SAC, DQN) for post-training of foundation models like LLMs and time series foundation models.
 • Implement distributed RL training pipelines using frameworks like Ray RLlib, Deepspeed, or custom solutions.
 • Design and implement benchmark pipelines for model evaluation.
 
 • 2. Align foundation models like LLMs and time series foundation models with specific areas/tasks through techniques like SFT, RL.
 • 3. Coding & Infrastructure: • Write production-grade Python code using PyTorch, numpy, and pandas.
 • Manage Linux-based clusters for distributed training and deployment.
 
 • 4. All other support required by the line manager if necessary.