字节跳动公有云机器学习系统工程师-训练方向
社招全职A197649地点：北京状态：招聘
任职要求
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； 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，较好的学习能力、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。 加分项 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。
工作职责
1、负责机器学习系统训练架构和产品的设计开发，支持火山方舟大模型平台和机器学习平台的产品业务； 2、负责充分利用各种异构计算（GPU、CPU、其他异构硬件）、存储（各种云存储）、网络（VPC、RDMA）等资源，支持主流的PyTorch、Megatron、TensorFlow等训练框架，支持大规模 LLM 预训练、Finetuning、强化学习等各种训练范式，支持大模型、自动驾驶、生信计算等各种业务场景下算法需求； 3、负责训练系统的产品化落地，打造算法工程师友好的、体验一流的公有云训练平台。
包括英文材料
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
Go+
https://www.youtube.com/watch?v=8uiZC0l4Ajw
学习Golang的完整教程！从开始到结束不到一个小时，包括如何在Go中构建API的完整演示。没有多余的内容，只有你需要知道的知识。
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
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/
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
编程规范+
[英文] Google Style Guides
https://google.github.io/styleguide/
Every major open-source project has its own style guide: a set of conventions (sometimes arbitrary) about how to write code for that project. It is much easier to understand a large codebase when all the code in it is in a consistent style.
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.
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.
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.
Kubernetes+
https://kubernetes.io/docs/tutorials/kubernetes-basics/
This tutorial provides a walkthrough of the basics of the Kubernetes cluster orchestration system.
https://kubernetes.io/zh-cn/docs/tutorials/kubernetes-basics/
本教程介绍 Kubernetes 集群编排系统的基础知识。每个模块包含关于 Kubernetes 主要特性和概念的一些背景信息，还包括一个在线教程供你学习。
https://www.youtube.com/watch?v=s_o8dwzRlu4
Hands-On Kubernetes Tutorial | Learn Kubernetes in 1 Hour - Kubernetes Course for Beginners
https://www.youtube.com/watch?v=X48VuDVv0do
Full Kubernetes Tutorial | Kubernetes Course | Hands-on course with a lot of demos
Docker+
https://www.youtube.com/watch?v=GFgJkfScVNU
Master Docker in one course; learn about images and containers on Docker Hub, running multiple containers with Docker Compose, automating workflows with Docker Compose Watch, and much more. 🐳
https://www.youtube.com/watch?v=kTp5xUtcalw
Learn how to use Docker and Kubernetes in this complete hand-on course for beginners.
分布式系统+
https://www.distributedsystemscourse.com/
The home page of a free online class in distributed systems.
https://www.youtube.com/watch?v=7VbL89mKK3M&list=PLOE1GTZ5ouRPbpTnrZ3Wqjamfwn_Q5Y9A
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.
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.
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A11907
1、负责机器学习系统资源调度的设计和开发，支持火山方舟大模型平台和机器学习平台的产品业务； 2、负责多机房、多集群环境下的，各种异构计算（GPU、CPU、其他异构硬件）、存储（各种云存储）、网络（VPC、RDMA）等资源的最优化编排调度，在严格的多租隔离环境下，支持各种离线训练、在线推理等负载场景的调度需求，并实现整体资源的合理化、最大化利用。
更新于 2024-06-28
社招
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
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
社招算法
1. 构建多云异构资源调度体系，整合多家云厂商的AI算力资源，设计优先级策略，实现跨平台资源池化管理和高效动态分配； 2. 设计数据智能路由方案，确保训练数据在混合云环境下高效流动，优化跨云数据同步效率； 3. 对接MLOps系统，实现训练任务编排、版本控制、模型监控等功能的深度集成； 4. 开发资源效能监控系统，实时追踪GPU利用率、任务排队时长等核心指标。
更新于 2025-04-08