该职位来源于猎聘 Algorithm Lead– End-to-End Model Location: Shanghai 工作地点：上海 Job Description
职位介绍 Key Responsibilities（主要工作职责）
- End-to-End Model Design &
- Innovation: Lead the architecture design, implementation, and optimization of next-generation End-to-End autonomous driving models, covering the full stack from perception, prediction, decision-making, to planning.
全过程模型设计与技术创新：主导下一代自动驾驶端到端模型的架构设计、落地与优化，覆盖感知、预测、决策至规划的全技术栈。
Generative AI Exploration: Develop and implement E2E algorithm solutions based on cutting-edge generative technologies, including VLM/VLA (Vision-Language/Action Models), LLMs (Large Language Models), and Diffusion Models.
生成式 AI 技术探索：基于视觉语言 / 视觉动作模型（VLM/VLA）、大语言模型（LLM）、扩散模型等前沿生成技术，研发并落地端到端算法方案。
Inference Optimization &
- Deployment: Spearhead model inference acceleration and performance optimization. Drive model lightweighting strategies for embedded platforms to resolve high latency and compute bottlenecks in real-world applications.
推理优化与部署：牵头模型推理加速与性能调优，针对嵌入式平台制定模型轻量化方案，解决实车应用中的高延迟与算力瓶颈问题。
Data Engine &
- Evaluation: Participate in the development of automated data pipelines and establish robust model evaluation metrics. Continuously iterate and enhance E2E model performance through closed-loop data mining.
数据引擎与效果评估：参与自动化数据链路搭建，建立完善的模型评估指标体系；依托数据闭环挖掘数据价值，持续迭代、提升端到端模型效果。
Cross-functional Collaboration: Collaborate closely with system architects and embedded engineering teams to ensure seamless model integration and deployment. Solve practical performance issues and continuously improve model effectiveness in actual road tests.
跨团队协作：与系统架构、嵌入式研发团队紧密配合，保障模型顺利集成与部署；排查实路测试中的性能问题，持续优化模型实际表现。
Frontier Tech Research: Stay at the bleeding edge of large-scale models and reinforcement learning (RL). Explore the migration and application of these advanced technologies within commercial vehicle driving scenarios.
前沿技术研究：紧跟大模型、强化学习（RL）领域前沿技术，探索上述先进技术在商用车驾驶场景中的迁移与落地应用。 Qualifications &
Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, Information Engineering, Electronic Engineering, Robotics, or a related field.
学历要求：计算机科学、人工智能、信息工程、电子工程、机器人学或相关专业，硕士及博士学历。
Experience（相关工作经验）:
3 years of hands-on experience in deep learning algorithm development, preferably within the autonomous driving or robotics industry.
具备 3 年及以上深度学习算法开发实操经验，有自动驾驶、机器人行业从业经历者优先。
A strong publication record in top-tier AI/CV conferences (CVPR, ICCV, NeurIPS, etc.) or proven success in relevant open-source projects is highly preferred.
在 CVPR、ICCV、NeurIPS 等人工智能 / 计算机视觉顶会发表过论文，或有知名开源项目落地成果者优先考虑。
Technical Expertise（专业技术能力）:
Proficient in Python and C , with extensive practical experience in deep learning frameworks such as PyTorch or TensorFlow.
熟练使用 Python、C ，精通 PyTorch、TensorFlow 等主流深度学习框架，具备丰富项目实操经验。
Deep understanding of mainstream algorithms in perception, prediction, and planning/control.
深入掌握感知、预测、规划与控制领域主流算法。
Solid grasp of Imitation Learning (IL) and Reinforcement Learning (RL) principles.
扎实理解模仿学习（IL）与强化学习（RL）原理。
Strong experience in model inference optimization, including model compression techniques (pruning, distillation) and quantization (INT8/FP16). Familiarity with high-performance inference frameworks like TensorRT, ONNX Runtime, or vLLM is highly desirable.
拥有丰富的模型推理优化经验，熟练运用剪枝、模型蒸馏等压缩技术以及 INT8/FP16 量化方案；熟悉 TensorRT、ONNX Runtime、vLLM 等高性能推理框架者优先。
Problem Solving: Excellent logical thinking, systematic problem-solving abilities, and strong fundamentals in data structures and algorithms. Passionate about autonomous driving technology and eager to tackle challenging real-world engineering problems.
解决问题能力：逻辑思维清晰，具备系统化问题解决能力，数据结构与算法功底扎实；热爱自动驾驶技术，愿意攻克各类实际工程难题。
Communication: Fluent in English and Mandarin, capable of effectively collaborating with global HQ and local cross-functional teams.
沟通能力：中英双语流利，可高效对接全球总部及国内各跨部门团队。
