Easy compared to others in the area. They asked about computing running mean and variance of some data. They also asked about logistic regression and overfitting in ML, which i thought was quite easy in practice
Interview questions [1]
Question 1
They asked about computing running mean and variance of some data.
The interview process comprised two distinct rounds. The first was a coding interview, designed to assess programming skills and problem-solving abilities. Following this initial stage, candidates participated in a virtual onsite session. This second round was more comprehensive, featuring two coding challenges that tested technical proficiency in real-world scenarios. Additionally, there was a behavioral interview aimed at evaluating the candidate's soft skills, workplace compatibility, and situational judgment. The final part of the process involved a machine learning design question, focusing specifically on building a recommendation system. Overall, the experience was highly engaging and provided a well-rounded assessment of both technical capabilities and personal attributes.
1. Resume discussion and system design 2. This interview involves solving coding problems that will help the team evaluate your ability to perform the day-to-day tasks of a PhD Research Scientist Intern on the XR Insight team.
Interview questions [1]
Question 1
high-level system design question to gain insight into how you approach a vague problem, identify trade-offs, design a solution, and anticipate potential pitfalls.
