I applied through college or university. The process took 4 weeks. I interviewed at C3 AI (Redwood City, CA) in Jan 2018
Interview
The overall interview process is smooth, well thought out, and you can count on team's support along the way. The interview consists of personal and technical parts.
First I was tested on technical abilities. I completed a HackerRank screen, a phone interview, and then onsite technical interviews. The HackerRank screen mainly tests your basic machine learning concepts and the phone interview tests how deep your knowledge is about different machine learning algorithms. After theses, I was brought onsite for three more technical interviews ranging from coding to testing more machine learning concepts.
After I passed the technical assessment, I was brought onsite again to interview with members from the management team. I was not tested on my technical abilities but this part of the interview enabled me to learn more about the company and where the management team is leading this company towards. As I progressed through the interviews, I actively gleaned more information of the company and it enabled me to make a better decision when deciding which company to join afterwards.
The last round was with the CEO. My HR contact was very helpful when arranging my interview to suit my schedule. Whenever I had questions, I could ask the data science team and they were always very helpful.
Interview questions [1]
Question 1
Why using random forest rather than other algorithms.
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