How to Answer ML Coding Interview Questions

The ML coding interview round lasts approximately 45 minutes.

During the interview, an ML engineer will ask you 1-2 questions that will assess both your knowledge of an ML framework (e.g. TensorFlow, PyTorch) and of a core ML concept in the team’s particular sub-field (e.g. Transformers, convolutional nets, data processing, ML evaluation systems, distributed ML). You will be expected both to implement it correctly and to explain how that component works and interacts with the larger system. If you finish early, there might be a follow-up question related to how to extend the system to a more complex or generalizable scenario.

A framework for answering ML coding interview questions

ML coding interview questions can feel intimidating because of the time limit and expectation to verbalize your thought process, which isn’t common in typical coding sessions.

A framework helps you stay focused, budget your time strategically, and communicate with the interviewer. This lesson will teach a simple framework to use in your interview.

The 4-step ML coding interview framework

An effective ML coding interview answer follows these steps:

• Step 1: Understand the problem. Ask clarifying questions, try a few toy examples, and ensure that you understand what the inputs/outputs are. (5-7 minutes)
• Step 2: Discuss the approach. Write out a high-level outline of your algorithm in pseudocode. Get buy-in/approval from the interviewer. (3-5 minutes)
• Step 3: Implement the algorithm. Decide on a particular ML framework (e.g. PyTorch or TensorFlow) and language (usually Python). Implement the algorithm, and talk out loud as you write the code. (20-25 minutes)
• Step 4: Test code and discuss results. Test your code, discuss the results, and note any significant takeaways. Check back in with the interviewer about any follow-up questions. (7-8 minutes)

While it’s important to integrate all the steps into a structured interview framework, you can adjust the time allotted for each step based on your interview style and the problem you're trying to solve.

Step 1: Understand the problem

Time estimate: 5-7 minutes

Often, the question may not provide all the information you need to solve the problem. Asking clarifying questions ensures you understand the full context of the problem. You can also make reasonable assumptions where applicable, but make sure to state these assumptions with the interviewer. Additionally, if certain unfamiliar terms are not defined, ask the interviewer to define them.

Step 2: Discuss the approach

Time estimate: 3-5 minutes

After understanding the problem, map out the approach and confirm the overall plan with the interviewer. This is a great opportunity to align with your interviewer on the approach so that you’re both on the same page. Take feedback and check in with your interviewer before you code the answer. Write pseudocode to make sure that you have a clear idea of how to solve the problem before you start writing the actual code.

Step 3: Implement the algorithm

Time estimate: 20-25 minutes

With a plan in place, you can now enter the core of the interview: implementing the algorithm. Be sure to verify and debug your code when writing it out. Explain your thought process clearly and in plenty of detail. Confirm that the output is as expected, based on the approach you discussed with the interviewer.

Be wary of sloppy coding. Whether you forgot to test throughout, didn’t consider edge cases, or coded something illegible, sloppy code won’t cut it in an interview scenario, regardless of how fast it is. Consider commenting on your code, both for yourself and the interviewer. Remember the context: you’re here to prove you can solve problems in a business environment.

Common mistakes include:

• Giving random variable names
• Using multiple naming conventions
• Creating unnecessarily complicated logic and implementation

Python
def euclidean_distance(x, y):
  return np.sqrt((x - y).T.dot(x - y))

def knn(X_train, y_train, X_new, k):
  distances = []

Python
distances = []
for X_i in X_train:
   distance = euclidean_distance(X_i, X_new)
   distances.append(distance)

Python
top_k_indices = np.argsort(distances)[:k]
top_k_labels = [y_train[idx].item() for idx in top_k_indices]

Python
# Ensure floating-point division by explicitly converting `k` to a float
  if sum(top_k_labels) > (k / 2.0):
    return 1
  else:
    return 0

Python
return np.sqrt((x1 - x2).T.dot(x1 - x2))

Step 4: Test code and discuss results

Time estimate: 7-8 minutes

After running the algorithm, discuss the results and any takeaways you'd be looking for. Additionally, discuss any tradeoffs in implementation or outcome that might affect the effectiveness or efficiency of your code.

Python
X_train = [
   [0, 0],
   [0, 1],
   [2, 0]
   [2, 2],
   [2, 1]
]

y_train = [0, 0, 0, 1, 1]

Common pitfalls

1. Skipping problem clarification. Failing to properly scope out the problem causes you to miss out on showing how much you know about the tech stack and the landscape your company is working with. You’ll also probably miss important details that will block you later on. Always begin by restating the problem statement and discussing ways to chunk it down.
2. Being quiet. Your interviewer is a huge resource. Make sure you don’t miss out on their advice as you work. Not talking through your thought process and not checking in frequently is a big missed opportunity. If you code a great solution in silence, you may have shown technical skills, but you’ve failed to show you can work on a real-world problem, which often involves other people. Practice talking while you code to get more comfortable with it.
3. Not being familiar with the algorithm and libraries. Unlike Leetcode-style interviews, you are expected to be familiar enough with the necessary APIs (i.e. NumPy, Pandas) to work with data and implement machine learning algorithms. Common operations (e.g. matrix multiplications) can delay you if you have not reviewed them beforehand. Review the few ML algorithms that can be reasonably asked in an interview to ensure you implement the algorithm correctly in the time limit.