Demand for Machine Learning Engineers’ has grown by 74% in the past 4 years, a trend that is expected to continue with the advent of the Fourth Industrial Revolution. Increasing demand for talent and the high salaries on offer led Indeed to name the ML Engineer as the job of the year in their annual report.
Given the complexity of the role and differing specializations, the interview process can be lengthy and cover a vast array of topics – making it difficult to know exactly what will come up at the interview stage.
At Alldus, we have worked a significant number of Machine Learning vacancies over the years and collated a lot of interview feedback along the way.
We’ve put together this list of some of the most commonly asked questions our candidates have faced, splitting them into 2 categories: General/Competency (with some tips on how to answer) and a list of the most commonly asked technical questions.
1. In layman’s terms, explain what Machine Learning is?
This is the interviewer testing your ability to take a complex/technical role and describe it in an easy-to-understand way.
This is important for a couple of reasons. For one, it shows you can explain complex processes in a way that even non-technical personnel can understand, something that is vital when communicating with wider stakeholders.
Quickbase have a great article with tips on how to communicate complex information in a more palatable way.
2. Have you read any interesting papers on Machine Learning recently?/What developments in the field do you find exciting?
This question is about exploring your interest in machine learning outside the confines of work.
It also demonstrates that you are keeping up-to-date with the latest developments in the field, something that is crucial in such a rapidly evolving discipline.
There are a variety of great free online resources that can be used to stay on top of the latest applications of ML:
3. How can the research you are doing add tangible value to real-world business cases?
This type of question is about demonstrating your ability to articulate how the work you are doing can add value to the business as a whole.
Rather than explaining in technical detail the work you did on developing and integrating an ML model for multi-label classification, provide outcomes of how this added value to the organisation and the bottom line.
Did the project improve Data accuracy by a certain percentage, saving the company money?
The inclusion of KPI’s and figures (either monetary or productivity-focused) can provide great insights into how the work you’re doing adds value to your employer.
4. In our industry (company-specific), what type of data do you think is most valuable?
This question is testing how well you understand (and how much you have researched) the challenges unique to your prospective employer and if you understand how the work you are doing links to overall business goals.
In order to answer this effectively, you should aim to gather as much information about the company as possible. Ask what projects the company is working on during the initial qualification call or at the first interview stage: what challenges are they trying to overcome and do they have any bottlenecks?
Google can also provide great insights into the challenges similar companies have faced and how machine learning is benefitting the industry.
5. Tell us about a time you experienced an unexpected problem during your last project and what steps you took to overcome it? What was the outcome?
The competency-based question is a fixture of interviews regardless of discipline. Machine Learning is no different!
This type of question analyses how you react when a problem arises (something that is inevitable in machine learning) and assesses your problem-solving ability by using real-life examples to form the basis for your answer.
The STAR technique (which we have discussed before) is a great way to answer competency questions and provides a framework from which you can build an answer.
Commonly Asked Technical Questions
- Discuss the different types of Machine Learning? (Supervised, Unsupervised & Reinforcement learning)
- What do you understand by “selection bias”?
- What is the difference between a Type I and a Type II error?
- What is the difference between L1 and L2 regularization?
- How does an ROC curve work?
- Explain the difference between inductive & deductive learning
- What is Object Detection in Machine Learning?
- What is overfitting, and how can it be prevented?
- Explain the Bias and Variance trade-off. Why is it important?
- Describe the difference between “likelihood” and “probability”
- What is a “training set” and what is a “test set” in an ML model?
- How would you build a Data pipeline?
- What is meant by a “Classifier” in Machine Learning?
- What are Support Vector Machines (SVM’s)?
- What are the advantages of SVM algorithms?
- What is Semi-Supervised learning?
- What is Deep Learning, and how does it differ from machine learning?
- How can Deep Learning be used to detect anomalies?
- What are the advantages of using a CNN (Convoluted Neural Network) for Image Classification?
- What is Cross Entropy?
- Explain Transfer Learning (How, when why it occurs)
- What is the difference between Numpty and Pandas?
- How would you handle missing or corrupted data in a dataset?
- What steps would you take if you had an imbalanced data set?
- What is the difference between Gradient Boosting & Random Forest?
Despite its length, this list represents just a small number of the potential questions that can come up during an interview.
We recommend thoroughly reading through the job description and taking note of the main technologies/responsibilities mentioned. From there, a more specific search can be conducted to identify some of the most frequently asked questions.
If you’re applying through an agency, you should always ask if they have had any previous candidates interview for the role? If so, what topics came up?