AI Engineer | LinkedIn | Virtual Onsite | Rejected
Summary
I had a mixed interview experience with LinkedIn. While I passed the initial rounds and received positive feedback on my ML theory and product vision, I was ultimately rejected without detailed feedback. The system design round likely played a key role in my rejection.
Full Experience
My interview process with LinkedIn started with a phone screen that covered both ML theory and light coding. I explained the attention mechanism and implemented an infinite data stream mean calculator using an incremental formula to avoid overflow.
For the AI coding round, I used an AI-assisted IDE to implement an LRU Cache data structure. I generated a Doubly Linked List with a HashMap, but the interviewer kept adding features, leading to over-engineering. I wasn't sure what was being evaluated in this round.
In the data structures and algorithms round, I discussed previous projects and validated a Binary Search Tree. I provided two solutions: an in-order traversal for O(N) time and a recursive approach for space optimization.
The ML fundamentals round focused on retrieval/ranking theory, imbalanced data, and model explainability. I demonstrated strong theoretical understanding, which the interviewer praised.
The system design round was challenging. I designed a Two-Tower Architecture for a Second Pass Ranker, but I made a critical mistake by using it for ranking instead of retrieval. I also incorrectly used softmax for a multi-label scenario, which should have been multi-label sigmoid heads.
Finally, the hiring manager round focused on product vision, behavioral questions, and my resume. While they complimented my diverse experience, they felt I needed to focus more on product-facing teams.
Interview Questions (4)
Infinite Data Stream Mean Calculator
Calculate the mean of an infinite data stream.
LRU Cache Implementation
Implement an LRU Cache data structure using AI tools and refine it. Later, add a validation method for keys.
Validate Binary Search Tree
Validate a Binary Search Tree (BST).
Second Pass Ranker System Design
Design a "Second Pass Ranker" for the Feed serving billions of users. Constraint: No pre-existing embeddings.