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Introduction to ML Coding Interviews

If the data science role you’re applying for heavily involves machine learning (ML) skills, you’ll likely receive ML coding interview questions similar to what an ML engineer would receive. These questions assess your technical problem-solving skills, knowledge of ML frameworks, and experience with the team’s sub-field.

This course includes an interview framework, a rubric explaining how you’re graded, mock interviews, and practice questions.

In this lesson, we give an overview of the interview round, what to expect, and how to prepare.

What to expect

Unlike software engineering interview questions, which focus on data structures and algorithms, ML coding interviews focus on building algorithms and data transformations. More specifically, there are generally three unique ways that the coding prompt will appear:

  • Write a common algorithm from scratch (e.g. k-means, knn). You'd be expected to build the algorithm using NumPy. This tests whether you remember basic algorithms and can implement them from scratch, typically using dummy data.
  • Given some data, provide an end-to-end solution and present metrics and reasoning. You'll be expected to transform data, choose a model(s) and metric(s), show some hyperparameter tuning, and explain how to search the hyperparameter space (e.g. random search vs. grid search). You’ll also typically visualize the data. For example, in a classification problem, you might see imbalanced labels. You’d discuss this observation and explain how it affects your decision on metrics, sampling, loss functions, etc.
  • Perform a common ML operation (e.g. 2d convolution, self-attention, batch norm). These questions will be in NumPy, test your knowledge of these operations, and assess your ability to perform these operations cleanly.

Almost all ML coding interviews are conducted in Python, and you will likely be at a disadvantage if you are not well-versed in Python.

Example questions include:

What interviewers look for

In the ML coding interview, you’re assessed on how well you:

  • Understand and solve the given problem
  • Understand the chosen ML framework and the team’s particular sub-field
  • Implement organized and accurate code
  • Communicate your logic
  • Display comfort and skill with ML algorithms

How to prepare

Brush up on some fundamentals in your ML framework of choice. Most ML start-ups and large companies use Python and PyTorch. Some additional helpful resources to learn include:

  • Our ML Concepts course: covers the fundamentals of ML models and algorithms. For example, our lesson on linear regression has almost everything you need to know about linear regression in an interview setting.
  • Our Python for data scientist course: covers data manipulation in Pandas and performing statistical analysis and experiments. Although this is not ML-specific, it is a great way to brush up your Python skills!
  • PyTorch tutorials: cover the fundamentals of data loading, training loops, neural network architecture implementations, and even reinforcement learning. In practice, most companies also use a wrapper on top of PyTorch, such as HuggingFace transformers.
  • HuggingFace courses: cover the essentials of how to use their transformers, datasets, and metrics libraries. Looking at some of their examples can be helpful too, so that you know how to implement real-world ML applications with their frameworks. After you’ve reviewed the fundamentals, practice implementing common algorithms (e.g. logistic regression, K-means) under a time limit, and practice working with NumPy arrays.

Lastly, check out the following resources to gain the high-level and implementation knowledge:

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