The technical question was a fairly basic data processing question, easily solved using Pandas.
Machine Learning Engineer Interviews
Machine Learning Engineer Interview Questions
Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
Top Machine Learning Engineer Interview Questions & How to Answer
Question 1
Question #1: What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?
How to answer
How to answer: Be prepared to talk about things like Type I and Type II errors, supervised and unsupervised machine learning, ROC curves, and other key parts of machine learning. Employers want to know you have a strong knowledge of the technical aspects of the job position.
Question 2
Question #2: How would you explain machine learning to someone who doesn't understand it?
How to answer
How to answer: Sometimes machine learning engineers have to work with people who aren't familiar with the technical aspects of the job. Use this interview question as an opportunity to show your strong knowledge of the position and your communication abilities.
Question 3
Question #3: How do you stay up to date with the latest news and trends in machine learning?
How to answer
How to answer: By talking about how you're up to date with the latest news and trends in machine learning, you can show an employer that you're engaged in the industry, a skilled researcher, and self-motivated.
8,212 machine learning engineer interview questions shared by candidates
One interview was basically a Machine learning course exam, asking about what I know about the basics of ML and metrics and so on. The seconed one was mostly detailed on my projects
Tell me about your experience
In the coding task I was asked to do a simple leetcode problem first, then a git merge and manual resolution of conflicts, and then the final part was a data analysis section. For the data analysis I had to download a dataset from Kaggle and visualise some trends in it.
Could you explain one of the latest data projects you have worked in?
1. Describe the bias variance tradeoff. 2. How would you measure the performance of a classifier? 3. What is an example of a problem where you would favor recall over precision and vice versa.? 4. Describe the main idea behind boosting along with some well known algorithms such as adaboost and xgboost. 5. Describe several options for embedding text data into a feature along with pros and cons of each. 6. Describe your favorite sorting algorithm
This is out of context, but one question I wrote down was, "What would you tell a CIO to ask a vendor when evaluating an off-the-shelf AI product?"
Initial screening was about my projects, future goals and motivations. Assignment was regarding problem with mnist.
Pros and cons of different generative models (e.g. graph diffusion, text-based transformers etc.) used in the biopharmaceutical industry. Ways to improve model throughput.
Regularisation selection of multiple features but few on informative data, ROC/AUC for binary classification and imbalance dataset
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