Screening Round
Questions:
- Project explainer for 10 minutes.
- 3 ML questions for 10 minutes (quick round).
- 2 coding questions related to each other + bonus coding question (also related).
- DFS question (with 2 parts).
- Optional Bonus question - follow up that had a DP solution (only idea discussed, not asked to code).
Candidate's Approach
No approach provided.
Interviewer's Feedback
No feedback provided.
Round 1: Onsite Coding
Questions:
- Coding #1: Implement a data structure that the candidate knew about but had not practiced before.
- Coding #2: A simple twist over a top-100 question from another company (Meta), an array type question.
Candidate's Approach
- Struggled in the first coding round due to nervousness.
- Performed well in the second coding round and solved an optional follow-up.
Interviewer's Feedback
No feedback provided.
Round 2: HM Interview
Questions:
- Discussed cross-functional communication, mentoring, technical depth, and conflict resolution.
- Asked about the candidate's tech stack.
Candidate's Approach
The candidate felt they did okay but less impressive than in other HM interviews. They were unprepared for the tech stack question.
Interviewer's Feedback
No feedback provided.
Round 3: ML Practitioner
Questions:
- Discussed research articles from the candidate's past work.
- Questions on ML topics, including:
- Explain transformer architecture.
- Explain contrastive loss.
- Questions on learning to rank algorithms.
Candidate's Approach
- Discussed a selected paper from graduate school, focusing on the main idea and architecture.
- Felt they did above average but missed 1-2 questions on ML topics.
Interviewer's Feedback
No feedback provided.
Round 4: ML System Design
Questions:
- Standard ML system design question.
Candidate's Approach
Recommended understanding a standard set of ML system design questions in depth. The candidate followed resources from https://bytebytego.com/intro/machine-learning-system-design-interview.
Interviewer's Feedback
No feedback provided.