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转码找工作的资料总结找工就业

胖娃娃阿夏
2022/1/19 · 发布于求职(非面经)版·36073
转码资料总结

最近成功上岸了,总结一下转码过程中收集和使用的学习资料,主要都是地里的帖子。今后跳槽估计还能用到😂 😅

目录:
0. 简历
1. BQ
2. 刷题
3. System Design
4. Machine Learning Design
5. 其他资料
6. Offer比较



0. 简历
  • 如何写求职简历的注意事项
  • 视频 How to Write a Great Software Engineer Resume
  • 视频 How to Write Your Best Resume for Software Engineering Jobs // Tips from an ex-FAANG recruiter
  • Resume Automated Screening (Google一下很多这种网站,比如 jobscan,可以用来优化简历)

1. Behavior Questions
我面的第一个公司是Amazon, 所以就按照16条LP准备了很多小故事, 后面其他公司也用的这些故事。

1.1 地里很好的帖子
  • 亚麻详细准备经过 BQ准备资料
  • Crack the Behavior Questions——关于Behavior Question的碎碎念
  • bq套路详解
  • Amazon onsite behavior question
  • Amazon的BQ总结
1.2 Youtube视频
  • 亚麻高频BQ 35道: 常见的BQ题基本都能在Dan Croitor的频道找到视频讲解,他会讲所谓的答题思路
  • Dan Lok
  • CareerVidz
1.3 亚麻的一些LP套路
一个故事套至少两个LP,比如
  • I think big and insist on the highest standards, so I am right (a lot).
  • I bias to action so I deliver results.
  • I have customer obsession, so when I meet challenges, I take ownership and dive deep.
  • I am always eager to learn and be curious, so I invent and simplify.
  • I have a backbone but I disagree and commit, so I earn trust from colleagues.
一些BQ问题的等价转换
  • meet tight deadline = deliver results = bias to action = adapt to changing demands
  • mistake = failure = miss deadline = decision you regret = apologize = would handle differently ---> 重点 what you learned
  • conflict = disagreement = don't like me —> Backbone, disagree and commit + customer obsession + earn trust
  • hard decision = take calculated risk = task with ambiguity = have multiple solutions = lack of data\info
  • everyone is your customer
  • Anything positive ---> because I have customer obsession, I take ownership, I think big, I insist on the highest standards
  • Anything negative --> because I missed customer obsession, I didn't take ownership ---> I have learned .....

2. 力扣刷题


我从七月中旬从0开始做Leetcode,到12月底一共做了595道(155 easy, 345 medium, 95 hard)。
前面300题做的非常慢,过了300之后开始慢慢有感觉了,能识别出来套路了,后面做题就越来越熟练了,计时做基本在20分钟内能做完一道medium,hard就全凭运气了

我的做题顺序如下
1. Leetcode出的Top Interview Questions: easy, medium, hard 3个collections
2. Leetcode上 Amazon, Facebook, Microsoft 最近6月的高频题(做完了大部分)
3. Google 前50道 高频题和 Leetcode上Mock Assement的所有Google Phone Interview 题和部分Onsite Interview题
4. 部分地里的面经题

不会做的题主要看discussion的高赞答案,然后自己总结。
刷题的总结见我的这个帖子: 力扣常见题目总结

3. System Design


这一块也是从0开始学,主要学习了以下资料。
3.1 Educative
  • Web Application and Software Architecture 101 讲的很通俗易懂,比grokking更适合零基础的我。
  • Grokking the System Design Interview:最主要的学习资料,看了好几遍,主要学了解题套路。
  • Grokking the Advanced System Design Interview: 没看完,主要看了Cassandra, Dynamo, Kafka这三节,对NoSQL有了稍微深入一点的了解
3.2 Youtube
  • 俄罗斯大叔: 主要学习了一下讨论trade off,这是grokking里面讲的比较少的。
3.3 地里的好帖子
  • 一篇文章解决所有system design面试
  • 关于所谓的System Design,我说点个人意见吧
  • 10道系统设计精选题
另外还有地里很多推荐的神书 Designing Data-Intensive Applications,我并没有看过,没时间了。

4. Machine Learning Design
主要学了:

4.1 Educative
  • 教程 Grokking the Machine Learning Interview
  • 文章 Cracking the machine learning interview: System design approaches。
  • 文章 ML Systems Design Interview Guide

不建议读Educative上的[Machine Learning System Design],太粗略笼统了

4.2 书
  • 读了这本书 Machine Learning Engineering by Andriy Burkov 的一部分
4.3 地里贴子
  • 浅谈ML Design推荐系统面试心得
  • ML design 面试的解题思路总结,十全大补tips
  • ML design 面试的答题模板,step by step
  • ML系统设计答题套路
  • Machine Learning Design的框架

5. 其他工具

5.1 模拟面试
  • Pramp 免费 peer mock interview
5.2 画图白板
  • whimsical
  • sketchboard
5.3 其他YouTube上很好的视频
  • How to Prepare for Technical Interviews, Part 1 - Coding
  • How to Prepare for Technical Interviews, Part 2 - System Design
  • How to Prepare for Technical Interviews, Part 3 - Behavioral
  • What no one tells you about coding interviews (why leetcode doesn't work)
  • The 10 Most Important Concepts For Coding Interviews (algorithms and data structures)
  • Systems Design Interview Concepts (for software engineers / full-stack web)

6. Offer
工资 Level.fyi
大厂 vs 小厂
  • 世界很大,厂子很多
  • 大厂不一定是NG最好的选择
  • 作为senior EM,实名劝新毕业生一句,有狗脸不要选别的
  • 再说如果NG有FG的offer 为什么最好选FG
  • 大厂是99%的NG最好的选择
谈判技巧
  • 分享自己negotiate offer的方法
  • sign on翻3倍,教你negotiate offer
城市比较
  • Numbeo
  • MyLifeElsewhere
  • areavibes
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