ML Engineer面试要求

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年份: 2019
月份: 12
公司: 其他公司
其他公司名称: -
面试阶段: OA 电面 
职位类型: Full-time (New Grad)
职位名称: Fullstack Engineer
The interview will last 6 hours and is broken down into 5 different interview panels with 1-2 people in each panel. One person will be the main interviewer and leading the meeting while the other person shadows and asks some questions.
Data Mining Product:
Familiarize yourself with LinkedIn.
Also, interviewers will likely ask you data-related questions about the projects they are working on.

Questions like: Designing and interpreting experiments to test variants of the product? Expect some questions regarding A/B testing, questions regarding which metrics would be best to optimize, and questions about how to best evaluate your experimental results.
Doing deep dives to understand more about how users use LinkedIn. Expect questions that test your ability to carry a data project from end-to-end, and to effectively and faithfully communicate your findings. Expect to discuss projects from previous experiences or your education and communicate what you were able to find and what you did.
Developing features for a data product, you may be asked to focus on processing large amounts of data, or be asked about your previous experience with solving large-scale, difficult, and custom data problems
Coding (done on a whiteboard).
Practice your coding skills. Don’t forget to practice coding away from the computer (e.g. on paper). Review the data structures you may never have used outside of school — binary search trees, linked lists, heaps. Be comfortable with recursion. Know how to reason about algorithm running times. You can generally use any “real” language you want in an interview
During the interview:
  • Make sure you understand exactly what problem you’re trying to solve. Even if you think your understand the problem I strongly encourage you to ask clarifying questions to confirm.
  • Make sure you explain your plan to the interviewer before you start writing any code, so that they can help you avoid spending time going down less-than-ideal paths.
  • Before declaring that your code is finished, think about variable initialization, end conditions, and boundary cases (e.g. empty inputs). If it seems helpful, run through an example. You’ll score points by catching your bugs yourself, rather than having the interviewer point them out.


Data Mining & Machine learning
All the applied machine learning modules mostly focus on supervised learning. The interviewer will present you with a prediction problem, and ask you to explain how you would set up an algorithm to make that prediction.
Machine learning theory
Here they will test your understanding of basic machine learning concepts, generally with a focus on supervised learning. You should understand:
  • The general setup for a supervised learning system
  • Why you want to split data into training and test sets
  • The idea that models that aren’t powerful enough can’t capture the right generalizations about the data, and ways to address this (e.g. different model or projection into a higher-dimensional space)
  • The idea that models that are too powerful suffer from overfitting, and ways to address this (e.g. regularization)

Brush through machine learning algorithms, but you definitely need to understand logistic regression, also prepare for some in-depth discussions of SVM’s.
Your background
You should be prepared to give a high-level summary of your career, as well as to do a deep-dive into a project you’ve worked on. The project doesn’t have to be directly related to the position you’re interviewing for, but it needs to be the kind of work you can have an in-depth technical discussion about.
Pointers to remember
·         Coding (whiteboard)
·         Applied machine learning/ data mining
·         Your background
·         Machine learning theory
Preparation Checklist
Here is a summary list of tips for preparing for data engineering interviews.
Coding (usually whiteboard)
Get comfortable with basic algorithms, data structures and figuring out algorithm complexity. Practice writing code away from the computer in your programming language of choice.
1.       Applied machine learning
·         Think about the machine learning problems that are relevant to LinkedIn
2.      Your background
·         Think through how to summarize your experience.
·         Prepare to give an in-depth technical explanation of a project you’ve worked on.
3.      Culture fit
  • Think about the problems LinkedIn is trying to solve, and how you and the team you’d be part of could make a difference.
  • Be prepared to answer broad questions about what kind of work you enjoy and what motivates you.

4.      Machine learning theory
  • Understand machine learning concepts on an intuitive level, focusing especially on supervised learning.
  • Learn the math behind logistic regression.
  • Get comfortable with a set of technical tools for working with data.

Interview Day
The interviews will last 6 hours and is broken down into 5 different technical panels with 1-2 people in each panel. One person will be the main interviewer and leading the meeting while the other person shadows and asks some questions. (Not necessary in the order below)
1. Host Manager Interview
2. Coding & Algorithms 1
3. Data Coding
4. Data Mining
5. Data Mining Product Design
6. Lunch
One of the interviews will be done by a:
Host Manager. This person may, or may not, end up being the final hiring manager, but will be able to provide you with information on the different projects, engineering team culture, challenges, etc. He/she will be asking you about your career history, your job search (why you’re looking, why is LinkedIn interesting, what technologies are you interested in), and an overview of interesting projects you worked on, and your involvement in these projects also HM can deep dive into projects you worked on, technologies used, and architectural decisions. Make sure you're able to speak of an interesting/challenging project you worked on and be able to explain the challenges faced, lessons learned, and technical details. They’ll be looking for excitement or interest in the projects you worked on because they want to work alongside engineers who are passionate about what they do. Start at a high level explaining the project (why was it needed, what was it used for, who would use it, etc) then explain what your team did, then what you were responsible for. Make sure you have a good understanding of the decisions that were made and a holistic understanding of the work.
One of the interviews will focus on coding questions. Just like in the phone interview, these will focus on CS fundamentals, data structures, and algorithms. You will be using the white board for the coding interviews. You want to approach these problems as if they were real-world problems. I recommend you do 2 things:
1. Ask a lot of questions before starting your implementation (ask clarifying questions, identify use cases, figure out test/edge cases)
2. Think out loud (they won't know what direction you're going unless you verbalize your thoughts)
During these interviews, the engineers will be looking for
1) Were you able to come up with a working solution
2) Cleanliness of code;
3) Time to completion;
4) How optimal your solution was (if they ask you if you can optimize your solution, look at your algorithm).
Here is a link you may have already received, but is very helpful for you to review prior to coming onsite: http://www.topcoder.com/tc?d1=tutorials&d2=alg_index&module=Static
Data Coding: This module is intended to assess a candidate’s fluency in translating data analytics and data mining ideas into code. Can you implement machine learning algorithms in code?
Data Mining: Focuses on Data Mining/ML/IR. Evaluation of your supervised ML knowledge, like SVM, regularization, overfitting, linear regressions, etc. Do you understand the statistics behind machine learning algorithms?
Data Mining Product Design: The purpose of this module is assess the ability to translate your Data Mining knowledge into a viable solution for solving a real world data product problem like Search Relevance in vertical search etc. Products we have at LinkedIn.
Please also have some questions to ask the interviewers (interview us too!). This not only shows that you are interested, but it will help you make sure this is the right place for you.
Here are a couple articles to get you more familiar with LinkedIn:



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