Ho presentato la mia candidatura online. La procedura ha richiesto 4 settimane. Ho sostenuto un colloquio presso C3 AI
Colloquio
I was so excited about C3.ai and the work they do but this was one of the worst interview experiences I ever had. I got through to the remote Skype interview and the first 30 mins was supposed to be a coding round. The interviewer joins the call 7 mins late and gives a coding question right away. I started coding in python and since I already lost the first 10 mins, I was more focussed on getting the logic right and coming up with an efficient solution. I ended up writing str1 instead of "string1" in two places by mistake and wrote " my_dict.get(key, 0) + num" instead of "my_dict[key] = my_dict.get(key, 0) + num". He stopped me right there and said "I have seen enough. Your logic is perfectly right but you need to work on your coding skills". Seriously ? I understand those were silly mistakes but I OBVIOUSLY would have taken care of them if I were to run the program.
I spent an entire week revising all the necessary Machine Learning Algorithms from scratch, mathematical derivations and the intuition behind each of them , studying test cases on their website and understand the data science aspect behind each of the test case. What an absolute waste of time!!
Domande di colloquio [1]
Domanda 1
Few multiple choice questions and a python program in the first round.
Ho presentato la mia candidatura online. Ho sostenuto un colloquio presso C3 AI (Singapore)
Colloquio
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.
Domande di colloquio [1]
Domanda 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)
Ho sostenuto un colloquio presso C3 AI (New York, NY)
Colloquio
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
Ho presentato la mia candidatura online. La procedura ha richiesto 3 settimane. Ho sostenuto un colloquio presso C3 AI (Londra, Inghilterra) nel mese di ott 2025
Colloquio
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.
Domande di colloquio [1]
Domanda 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