Rubric for ML Coding Interviews

In an ML coding interview, a successful candidate should be able to demonstrate expertise in the following signals:

1. Problem understanding. assesses your ability to identify concrete requirements from the prompt.
2. Technical skills. assesses your ability to execute organized and accurate code.
3. Rationale. assesses your logic and justification for your technical approach to the problem.
4. Communication. assesses your ability to communicate your design choices, discuss technical concepts, and collaborate with the interviewer.

An interviewer will assess these signals using a rating scale of “very weak” to “very strong.”

The overall rating of the rubric signals translates to:

• Very Weak: no hire.
• Weak: no hire, but interviewer can be convinced if candidate did exceptionally well in other interview rounds. Can lead to “downleveling.”
• Strong: hire, but interviewer may be convinced to no hire if candidate did poorly in other rounds.
• Very Strong: strong hire, interviewer will advocate for the candidate, even if other rounds went poorly. Can lead to “upleveling.”

In the sections below, we’ll identify what a weak vs. strong answer looks like for each rubric signal.

Problem understanding

• Very Weak: No clarification of the problem statement. Jumps right into coding without asking any questions.
• Weak: Some attempt at clarification of the problem, but not headed in the direction of the solution. May ask the right questions, but doesn’t communicate with the interviewer clearly.
• Strong: Asks relevant questions related to the implementation of the algorithm. Clarifies requirements with the interviewer successfully.
• Very Strong: Asks all the relevant questions related to the algorithm. Clearly identifies the answers given by the interviewer and notes them in their implementation.

Examples that ace this signal:

• Asking what the expected inputs and outputs are. For example, clarifying whether the input is given as a matrix and whether it is a classification or regression problem.
• Clarifying the logic of the algorithm. For example, asking about the stopping criteria for finding clusters in the K-means algorithm.

Technical skills

• Very Weak: Unable to implement any portion of the algorithm correctly. Code is unverified.
• Weak: Makes some progress on a solution, but some portions may be incorrect or incomplete. Code is unverified.
• Strong: Implements the solution correctly. Does basic verification on an example to show that the code is correct.
• Very Strong: Implements the solution in its entirety and verifies that it is correct on multiple examples.

Examples that ace this signal:

• Verifying the solution with test cases. If no test cases are given, the candidate creates their own.
• Writing functions to separate out different pieces of logic. For example, writing a separate function to compute the Euclidean distance between two data points.

Rationale

• Very Weak: Gives no justification for decisions being made during implementation.
• Weak: Gives some justification for making decisions, but justifications are either vague or incorrect.
• Strong: Gives generally sound justification for making decisions in implementation.
• Very Strong: Gives thorough justification for most, if not all decisions made in implementation.

Examples that ace this signal:

• Explaining why a certain function should be separated out, rather than included inside the function.
• Clarifying why an algorithm works the way it does. For example, explaining why K-means recomputes the mean of each cluster after every iteration.

Communication

• Very Weak: Mostly silent during implementation. Code and reasoning are almost entirely unclear.
• Weak: Makes some attempts at communicating but is scattered and unclear.
• Strong: Communicative throughout the interview and generally proactive in justifying the approach.
• Very Strong: Explains almost everything performed very clearly. Remains engaged and responds thoughtfully to questions or feedback from the interviewer.

Examples that ace this signal:

• Explaining line by line what each code component does while writing an algorithm.
• Responding and engaging immediately when an interviewer interjects.
• Walking through test cases by calling out the inputs and expected outputs.