J'ai postulé en ligne. Le processus a pris 4 semaines. J'ai passé un entretien chez Quantiphi (Boston, MA)
Entretien
Very very bad experience. Their HR partners and even the cofounder try to undervalue you saying that you're not good enough - very cheap way to make the candidate feel like they are not worthy enough to ask for a good salary. One of them literally called me up one fine morning and started yelling at me, even after making the offer. The other reviews seem to tell the same problem. If you have other options, avoid these guys.
There is only one round of technical interview and the interviewer seemed confused (like what is this term) when I said the term "generative models" during an answer to a question. Other people who interviewed me didn't seem to have an ML background besides having 'heard' all the usual jargons somewhere. No statistics or math or CS questions - These kinds of interviews makes you question the credentials of the people working there as well.
Questions d'entretien [1]
Question 1
An initial round (background/ profile based), a simple classification problem as homework, a technical interview (mostly based on how the solution was framed for the homework), an interview with a non-technical person, and a final round with their co-founder.
1. Why does multicollinearity happen in regression?
2. Working of boosted trees
3. Types of regularization
4. Overfitting and underfitting
J'ai postulé via un établissement d'enseignement supérieur ou universitaire. J'ai passé un entretien chez Quantiphi (Mumbai)
Entretien
4 Rounds
1 - Test -> Aptitude, CSE Fundamentals, Coding
2 - Technical Round 1: Resume based questions and some fundamentals of ML and AI
3 - Technical Round 2: Resume based questions and indepth questions on architectures, frameworks and fundamentally important parts
4 - HR
J'ai passé un entretien chez Quantiphi (Palo Alto, LA)
Entretien
Pretty easy basic ml question and transformer architectures , no leetcode or ml system design questions, Transformer architecture encoder decoder and autoregressive models Transformer architecture encoder decoder and autoregressive models
Questions d'entretien [1]
Question 1
Transformer architecture encoder decoder and autoregressive models
The process has three rounds: one aptitude test, one technical round on ML basics, algorithms, logical reasoning puzzles, project-related questions, and one HR round focusing on reasoning behind ML model choices.
Questions d'entretien [1]
Question 1
ML algorithms and its working based on projects in resume.
