小鹏汽车Research Scientist (World Model)
任职要求
1、计算机、电子工程、人工智能等相关领域硕士及以上学历; 2、具有扎实的机器学习算法基础,在 AIGC、RL、Robotics、3D Vision等相关专业领域有研究经验等相关专业领域有研究经验,曾以第一作者身份在CVPR/ECCV/ICCV/CoRL/ICRA/NeurIPS/ICLR/ICML/SIGGRAPH等顶会顶刊上发表过论文; 3、熟练使用PyTorch/TensorFlow等深度学习框架,具备良好的代码实现能力; 4、具有良好的团队合作能力和沟通能力。 加分项 1、计算机、电子工程、人工智能、机器人等相关领域博士学历; 2、有多模态、大模型、机器人相关研究和项目经验,有国际影响力的论文主要作者或项目主导者; 3、具有优秀的代码能力,如ACM/ICPC、NOI/IOl、Top Coder、Kaggle等比赛获奖者; 4、具备解决复杂问题的经验,并能比较各种解决方案,并根据不同的视角确定前进方向。具备基于机器学习和/或深度学习方法构建系统的经验。
工作职责
1、构建行业领先的世界模型,为具身场景提供生成式仿真能力。强化世界模型的长程时空记忆,物理属性模拟能力,实现可泛化、可落地、可scaling的世界模型,形成持续的技术影响力并引领国际行业发展。
1、构建行业领先的世界模型,为具身场景提供生成式仿真能力。强化世界模型的长程时空记忆,物理属性模拟能力,实现可泛化、可落地、可scaling的世界模型,形成持续的技术影响力并引领国际行业发展。
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 • 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.
Model Optimization & Deployment: Design and implement efficient workflows for training, distillation, and fine-tuning Small and Large Language Models (SLMs), leveraging techniques such as LoRA, QLoRA, and instruction tuning. Apply model compression strategies—including quantization (e.g., GPTQ, AWQ) and pruning—to reduce inference costs and improve latency. Optimize LLM inference performance using frameworks like vLLM and TensorRT-LLM (TRT-LLM) to enable scalable, low-latency deployment. Build robust and scalable inference systems tailored to heterogeneous production environments, with a strong focus on performance, cost-efficiency, and stability. Evaluation & Data Management: Develop evaluation datasets and metrics to assess model performance in real-world product scenarios. Build and maintain end-to-end machine learning pipelines encompassing data preprocessing, training, validation, and deployment. Cross-functional Collaboration: Collaborate closely with product managers, engineers, and research scientists to translate business needs into impactful AI solutions, driving real-world adoption and seamless product integration.