I just finished a walk with students Shopify ML VO, the overall process is quite compact. The three rounds of interviews were all completed within two days, and the pace was relatively fast. Compared with some companies where rounds are separated by several days and the whole process drags on for weeks, it is easier to complete this continuous interview.
The interview content basically focused on things related to machine learning, such as ML modeling ideas, projects I have done, and ML system design. The entire session did not have a question-answering session like algorithm questions. It was more like discussing how to do ML in actual business.
Shopify ML VO process structure
Three rounds of technical aspects
There are three rounds of technical aspects in the entire Virtual Onsite, each round is about 45 minutes.
The structure of the three wheels is relatively clear:
- ML Modeling
- Technical Deep Dive
- ML System Design
The interview format is usually a main interviewer plus a shadow interviewer, and the rhythm is more like technical communication than traditional interview questions and answers.
Round 1: ML Modeling
Scene question background
The first round is a typical machine learning modeling scenario question. The background of the question comes from e-commerce logistics:
Design a model to predict the delivery time of an order. This problem is essentially a delivery ETA prediction task, which needs to be dismantled from a complete machine learning modeling perspective.
Problem definition
First, the forecast goals need to be clearly defined. There are two common ways:
- Predict the specific delivery time of goods
- Predict the length of time from order placement to delivery
In actual modeling, the second method is usually used, which is to predict delivery duration, so that the problem is more standard and easier to handle.
Round 2: Technical Deep Dive
Project experience discussion
The second round is mainly an in-depth discussion of machine learning projects. The interviewer will ask the candidate to choose an ML project that he is most familiar with and talk about it from background to implementation. The entire discussion usually revolves around several aspects:
What business problem does the project solve?
Data sources and data size
Data cleaning and feature engineering
Reasons for model selection
Have you done any model comparison experiments?
If the project involves real business scenarios, the interviewer will often continue to ask about the situation after the model is launched, such as model update strategies or performance monitoring methods.
Round 3: ML System Design
System design scenario
The final round is machine learning system design. The background of the topic is to design an automatic classification system for e-commerce products. There are a large number of product categories on the platform, approximately more than 7,000 categories, and it is necessary to automatically classify products through machine learning models.
Enter data
System input typically includes:
Product title
Product description
Product image (if present)
This information can serve as the main source of features for the model.
Modeling scheme
A more common solution is the text classification model. You can start with a simple solution, such as: TF-IDF + Logistic Regression
Then we discuss more complex models, such as Transformer-based text classification models. If the product contains pictures, you can also consider a multi-modal model that combines text and images.
Some experience in interview preparation
We have been building a large factory here for a long time. Interview assistance services , many ML/DS/SDE interviews actually have a fixed discussion framework. It has helped many students successfully pass VO interviews in ML, Data, and SDE directions. If you are currently preparing for technical interviews with companies such as Shopify, TikTok, Amazon, etc., understanding this interview assistance method in advance can often significantly reduce on-the-spot stress and make it easier to stably display the prepared content.
