Job Interview Questions for Machine Learning Developers

If you’re interviewing for a machine learning development role, chances are good that you’re already a strong tech generalist, well-prepared for a highly technical conversation with a recruiter or hiring manager. But during an in-person interview, the questions will only get more and more technical and detail-oriented; if you want to land the job, you’ll need to impress your prospective employer with your grasp of core concepts.

Jagruti Shah, senior technical recruiter at the Palo Alto, Calif.-based ThoughtSpot, a technology company that produces business-intelligence analytics search software, assesses candidates to see if they have a holistic understanding of the applications they’d be handling. During an interview, she wants candidates to not only articulate the reasons behind their answers, but also show how their actions or ideas translate into effects on the machine-learning process.

Dice Interview Qs IconAside from a thorough knowledge of the fundamentals of the role, Enriko Aryanto, CTO and co-founder of the Redwood City, Calif.-based QuanticMind, a data platform for intelligent marketing, wants candidates to demonstrate a thorough knowledge of the role’s fundamentals. He also likes it when candidates utilize critical thinking to come up with practical answers to problems, rather than spinning up overly complex solutions. The best candidates, he observes, always use examples of how they solved applicable machine learning problems in previous positions.

With all that in mind, here are some sample interview questions to consider:

“What is the difference between bias and variance?”

What Most People Say:

“Bias and variance is supposed to set the algorithm designer,” or “Bias of an estimator is the difference between the estimator’s expected value and the value of the parameter being estimated. Variance of an estimator is a measure of how far values of the estimate can take away from its value.”

What You Should Say:

“Bias comes as a consequence of a model underfitting some set of data, whereas variance arises as the result of overfitting some set of data.”

Why You Should Say It:

It’s a simple answer, but avoids the textbook responses given by many candidates. Interviewers want to know if you understand the impact that overfitting or underfitting can have on a machine learning application. Developers who want to work on machine learning need to have a firm understanding not only of coding, but of each of the components that go into creating a successful machine-learning application.

“Which do you think is more important: model accuracy or model performance?”

What Most People Say:

When faced with this question, both Shah and Aryano note, it’s very common for candidates to try to justify the need for both accuracy and performance, and give examples of ways to improve both.

What You Should Say:

“While both accuracy and performance are of course important, and subjective to the specific application you’re building, accuracy is more important in general. If your machine learning application provides inaccurate information, it doesn’t matter how quickly it does it.”

Why You Should Say It:

Every machine learning application is different, which is why the answer should be tailored to the applications you’d be building. For example (per Shah), any product ThoughtSpot builds must deliver completely accurate information, or it won’t be useful to their customers.

“How does deep learning contrast with other machine learning algorithms?”

What Most People Say:

“Deep learning represents a more complex or sophisticated version of machine learning.”

What You Should Say:

“Deep learning is an approach to machine learning wherein the system learns the model as a neural network. If we’re addressing the algorithms specifically, it should be noted that deep learning algorithms learn meaningful features on their own, without requiring any manual feature selection.”

Why You Should Say It:

It shows that you actually know what deep learning is. Good machine learning developers understand deep learning conceptually, as well as the impact of using algorithms on problems such as feature selection.

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