a recruiter call, followed by a technical screening on data structures and ML fundamentals. The final round includes coding challenges, system design discussions, and ML case studies, often focusing on model deployment and scalability.

The top management have no proper background in ML but think they are doing ML. They are a data science shop with non publication or patent history. They ask questions on regression which is fine but reject candidates if they have forgotten about L1/l2 optimizations. Even principal scientists who are well known in the industry get rejected because of this. I wouldn’t have joined anyway as that would degrade my resume and also end up working with incompetent people no strong theoretical or production backgrounds in real ML.