小鹏汽车强化学习系统工程师
社招全职地点:深圳 | 上海状态:招聘
任职要求
1. 有机器学习、大数据平台的工程架构落地经验,熟练掌握常见的分布式训练、计算框架(pytorch/tensorflow/ray/spark/flink)原理及工程实现,熟悉GPU、大模型相关软硬件技术栈; 2. 有计算产品落地经验(最好是tob paas/saas 项目或公有云项目,深度使用经验也算),对该领域用户画像和用户故事有深入理解,有打造世界级产品的热情; 3. 熟悉NLP、CV相关的算法和技术,熟悉大模型训练、RL算法者优先; 4. 有以下某一方向领域的经验:CUDA,RDMA,AI Infrastructure,HW/SW Co-Design,High Performance Computing,ML Hardware Architecture (GPU, Accelerators, Networking),ML for System,Distributed Storage; 5. 熟悉开源的RL训练框架,例如RL lib、VERL、OpenRLHF等。
工作职责
1. 熟练掌握Linux环境下的Go/Java/Python等1-2种语言; 2. 具备扎实的计算机科学功底和编程能力,熟悉常见算法和数据结构,具有良好的编程习惯; 3. 熟悉至少一种主流的机器学习框架(TensorFlow / PyTorch 或其他自研框架); 4. 熟悉 Kubernetes 架构和生态,熟悉 Docker/Containerd/Kata 等容器技术,有丰富的云原生机器学习系统实践和开发经验; 5. 掌握分布式系统原理,参与过大规模分布式系统的设计、开发和维护,熟悉Ray; 6. 有优秀的逻辑分析能力,能够对业务逻辑进行合理的抽象和拆分; 7. 有强烈的工作责任心,较好的学习、沟通能力和自驱力,能够快速的响应和行动; 8. 有良好的工作文档习惯,及时按要求撰写更新工作流程及技术文档。
包括英文材料
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
Ray+
https://github.com/ray-project/ray
Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
https://www.youtube.com/watch?v=FhXfEXUUQp0
In this video, I'll teach you everything you need to know about Apache Ray!
https://www.youtube.com/watch?v=fMiAyj2kgac
Using powerful machine learning algorithms is easy using Ray.io and Python.
https://www.youtube.com/watch?v=q_aTbb7XeL4
Parallel and Distributed computing sounds scary until you try this fantastic Python library.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
PaaS+
https://www.ibm.com/cn-zh/think/topics/paas
平台即服务 (PaaS) 是一种云计算模型,提供完整的按需云平台(硬件、软件和基础设施),用于开发、运行和管理应用程序。
https://www.ibm.com/think/topics/paas
https://www.youtube.com/watch?v=QAbqJzd0PEE
SaaS+
https://www.ibm.com/cn-zh/think/topics/saas
软件即服务 (SaaS) 是一种基于云的软件交付模式,服务提供商借此托管应用程序,并通过互联网向用户提供这些应用程序。
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
CUDA+
https://developer.nvidia.com/blog/even-easier-introduction-cuda/
This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
https://www.youtube.com/watch?v=86FAWCzIe_4
Lean how to program with Nvidia CUDA and leverage GPUs for high-performance computing and deep learning.
相关职位
社招后端开发
【职位描述】 1、设计和实现机器学习平台业务系统, 包括工具链/组件等AI基础设施, 落地业务功能需求; 2、高效优化和部署 计算机视觉、语音识别、语音合成、自然语言处理 等业务模型; 3、与公司各算法部门深度合作, 分析业务性能瓶颈和系统架构特征, 软硬件结合优化, 实现极致性能。
校招
1. 熟练掌握Linux环境下的Go/Java/Python等1-2种语言; 2. 具备扎实的计算机科学功底和编程能力,熟悉常见算法和数据结构,具有良好的编程习惯; 3. 熟悉至少一种主流的机器学习框架(TensorFlow / PyTorch 或其他自研框架); 4. 熟悉 Kubernetes 架构和生态,熟悉 Docker/Containerd/Kata 等容器技术,有丰富的云原生机器学习系统实践和开发经验; 5. 掌握分布式系统原理,参与过大规模分布式系统的设计、开发和维护,熟悉Ray; 6. 有优秀的逻辑分析能力,能够对业务逻辑进行合理的抽象和拆分; 7. 有强烈的工作责任心,较好的学习、沟通能力和自驱力,能够快速的响应和行动; 8. 有良好的工作文档习惯,及时按要求撰写更新工作流程及技术文档。
更新于 2025-04-28