Me postulé a través de un reclutador. Acudí a una entrevista en TikTok (Singapur)
Entrevista
Each round typically includes one or two LeetCode-style algorithm questions, usually focusing on data structures like arrays, hash maps, or binary trees.
In addition to coding, they also ask some basic but fundamental machine learning and deep learning questions — for example, explaining how dropout works, the role of batch normalization, differences between SGD and Adam, or how overfitting can be handled.
Sometimes, they might dive deeper into topics like activation functions, loss functions, or model regularization techniques. The difficulty is usually moderate, but they do expect clear and structured explanations.
Preguntas de entrevista [1]
Pregunta 1
introduce dropout
why BN works, why BN need reshift and rescale
Surprisingly straightforward — I expected a tougher challenge for a machine learning role. After a quick recruiter screen, the first technical round focused on implementing K-means clustering, which felt familiar. Handling edge cases for empty clusters was tricky, though. What really helped me prep were the algorithm explanations on PracHub, which gave me confidence going in. The final interviews were a mix of problem-solving and behavioral questions, and in the end, I received an offer that I accepted. Overall, it was a decent experience.
Preguntas de entrevista [1]
Pregunta 1
implementing K-means clustering from scratch and handling empty cluster edge cases
three rounds, each has coding + ml basic + resume related questions
understand all the details in resume is important, since might go very deep down the project you have worked on
The first round was mainly a CV walkthrough.
The second round focused in depth on one specific project.
Both rounds also included LeetCode medium-level problems.
The third round was with the hiring manager with both project and problems for their business.
