I applied online. The process took 2 months. I interviewed at C3 AI in Jan 2022
Interview
Cold and industrial with "no-reply" emails disregarding the humans behind emails. That captures the entire interview process for me.
Two months after my application, I received an email out of blue that I was fast-forwarded to on-site.
Got a totally negative vibe when chatting to different people during the on-site interviews, with an emphasis on "we are so smart".
My second interviewer showed up 9 minutes late to a 30-minute interview. Started twisting simple stats questions in ways that were inaccurate. I was not able to understand what he was really after nor I could correct him because interview is not a place to argue with the interviewer. One of my worst interview experiences trying to answer the question while the inexperienced interviewer was trying to outsmart me by twisting simple questions with a smirk on his face.
Interview questions [1]
Question 1
How do you forecast sales of a product when you have three tables of data on sales, promotions and google analytics?
Are two independent variables necessarily uncorrelated and vice versa? (I think the true question that the interviewer could not spell is this "does lack of linear correlation between two variables mean they are independent?
How do you compute a non-parametric CI for parameters of a model?
Answer is bootstrapping and taking 2.5 and 97.5 percentiles. But again the interviewer was trying to twist everything to an incomprehensible level for himself and me.
I applied online. I interviewed at C3 AI (Singapore)
Interview
Hackerrank --> three tech interviews (proceed to the next one if you pass the current one) each round is 1 hour long --> hiring manager interview (1 hour)--> VP interview.
Interview questions [1]
Question 1
tech interviews: 1) (1 hour) traditional ML based case study, 2) (1 hour) ML concept deep dive, and 3) (1 hour) coding (leet-code medium)
Resume screening -> technical assessment -> 4 rounds of interviews:
- personal projects, simple questions not there to trick you
- situational questions: "what would you do if..."
- machine learning: starts from the very basics (stats and probabilities) to more up to date models
- coding: medium leet code
I applied online. The process took 3 weeks. I interviewed at C3 AI (London, England) in Oct 2025
Interview
I applied directly after seeing a job advert on LinkedIn. There are MCQ and coding assessment on Hackerank, followed by a screening interview. It all went well and got invited to the technical day.
To prepare for the technical interview, I went through all materials and questions shared by others on this website and once I was half way, I noticed that the questions tend to be similar, except the pairwise coding. I recommend you go through questions here to be better prepared for the technical day.
The interview was generally okay and the team was nice. Started off with Case Study (30 mins); followed by ML questions (30 mins); and finally coding (1 hour). There is barely time in-between to switch so expect to transition very quickly. For the case study, think out loud it helped me to figure the actual problem, as they only share the problem and you figure the rest out.
The coding was fair, I had done a couple of Leetcode but they started off with Linear regression etc, kinda caught me off guard and wasted 35 mins on it. Though the program ran, the interviewer said there isn't enough time to complete second question, and we shared our coding experiences and clarity on a few questions. I am pretty confident in stats and ML knowledge but the issue could have been coding; so make sure you are up to speed with anything that can be thrown at you.
Two days later I received a rejection email. No reason after having spend so much time is a bit disrespectful but we move on.
Interview questions [1]
Question 1
Case study: Waste reduction in chain stores. They simply stated that and I described it as a demand forecasting problem that can be solved with Linear Regression. Besides clarification questions, It was fine and they took it.
MLQ
1. Difference between Supervised and Unsupervised Learning, and give examples
2. Difference between bagging and boosting;
3. Bias and variance, and explain in the context of Bagging/boosting
4. Performance metrics; what does AUC mean, interpret AUC of 50%
5. Gradient descent
6. Overfitting and Underfitting and how to overcome them in Decision Trees
Coding: Implement linear regression, numpy, and plotting importance scores