西门子西门子中国研究院 人工智能工程师
任职要求
1. 计算机科学、人工智能、软件工程或相关专业本科及以上学历。2. 精通 Python 或其他适合 AI 系统开发的编程语言。3. 具备扎实的计算机基础,熟悉数据结构、算法、操作系统、网络与软件工程实践。4. 熟悉 LLM 与 Agent 的核心机制,包括 Prompt Engineering、Context Management、Tool Use、Memory、Planning、Subagent、Multi-Agent 等。5. 熟悉 AI 系统常见组件与接口,如向量检索、知识库、RAG、函数调用、工作流编排或 MCP 类工具协议。6. 具备良好的系统设计与问题排查能力,能够独立推进复杂工程问题定位和解决。7. 具有 AI Agent、Copilot、智能助手、自动化工作流或类似系统的研发经验。8. 具有 Agent 框架接入、执行环境搭建、评测系统建设或工具生态集成经验者优先。9. 有与研究团队、算法团队或产品团队深度协作经验者优先。10. 有真实业务…
工作职责
你将在这些领域发挥影响: 1. 设计与开发 Agent Harness 核心能力• 负责 Agent 执行框架、任务编排、上下文管理、工具调用、记忆机制和运行时能力的设计、开发与持续优化。2. 集成模型与外部工具生态• 对接 LLM API、检索系统、知识库、业务系统接口及各类工具协议,提升 Agent 在复杂场景下的可用性与稳定性。3. 构建评测与反馈闭环• 参与 Agent 评测体系、数据采集、实验验证和线上反馈分析,持续优化 Agent 在真实任务中的成功率、效率和用户体验。4. 推动 Harness 与模型协同演进• 与模型训练、算法和应用团队协作,从 Harness 侧推动 Prompt、Context、Memory、Planning、Tool Use 等机制的工程化落地与迭代。5. 建设工程基础设施• 参与 Agent 运行环境、调试工具、日志追踪、可观测性、配置管理和部署链路建设,保障系统稳定交付。6. 支撑前沿探索与产品落地• 结合真实业务任务与用户反馈,快速验证 Harness 新能力,并推动相关能力在产品或内部平台中落地
We empower our people to stay curious and innovative in a fast-evolving world. We’re looking for individuals who are eager to push boundaries, learn continuously, and create meaningful impact both now and in the future. Does that sound like you? Then we’d love to have you join our dynamic and diverse global team. DAI AIX – AI Acceleration and Exploration, is at the forefront of Data Analytics and AI research within Siemens’ global technology network, driving innovation, collaboration, and transformative applications for our customers. As part of our team, you’ll engage in cutting-edge applied research and development.We are currently seeking an NLP/LLM/Agent Engineer/Researcher to work on the development and deployment of next-generation language-related applications and intelligent agents. The focus of this role is advancing the capabilities of large language models (LLMs) and their integration into real-world applications such as autonomous agents and industrial workflows. You will design and implement advanced algorithms, optimize LLM architectures for specific use cases, and develop scalable solutions that drive tangible outcomes in industry. You'll make an impact by • Research on state-of-the-art data analytics & AI technologies on a general range. • Mainly focus on modern foundation model applications in industrial scenarios1. Context engineering for foundation models2. Development of agent systems for industrial applications3. Task-specific model finetuning • Partially work with multi-modal applications • Participating in both internal & external research projects • Assist deployment of customer development/deployment project
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.