## Round 1 - MLE Coding ## Problem Implement the backward pass (backpropagation) for a single fully-connected layer with ReLU activation. You are given the gradient flowing in from the next layer and must compute the gradients with respect to the layer's weights, biases, and input. ```python import numpy as np def relu(x: np.ndarray) -> np.ndarray: ... def relu_grad(x: np.ndarray) -> np.ndarray: ... # 1 where x>0, else 0 def linear_backward( dout: np.ndarray, # gradient from upstream, shape (batch, out_features) x: np.ndarray, # input to this layer, shape (batch, in_features) W: np.ndarray, # weights, shape (in_features, out_features) b: np.ndarray, # biases, shape (out_features,) pre_activation: np.ndarray # Wx + b before ReLU, shape (batch, out_features) ) -> tuple: # Returns (dx, dW, db) ... ``` ``` Shapes check: dout: (32, 128) x: (32, 256) W: (256, 128) pre_activation: (32, 128) dx: (32, 256) <- gradient w.r.t. input dW: (256, 128) <- gradient w.r.t. weights db: (128,) <- gradient w.r.t. bias ``` ## Follow-ups 1. What does multiplying `dout` by `relu_grad(pre_activation)` represent in the chain rule? 2. Why is `dW = x.T @ delta` and not `delta @ x.T`? 3. How would the backward pass change for sigmoid activation instead of ReLU? 4. How would you numerically verify your gradient implementation?

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ML Puzzle Coding - Implement Neural Network Backpropagation Step

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Round 1 - MLE Coding Problem Implement the backward pass (backpropagation) for a single fully-connected layer with ReLU activation. You are given the gradient flowing in from the next layer and must compute the gradients with respect to the layer's weights, biases, and input. Follow-ups What does multiplying dout by relu_grad(pre_activation) represent in the chain rule? Why is dW = x.T @ delta and not delta @ x.T? How would the backward pass change for sigmoid activation instead of ReLU? How wou…

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This is a candidate experience report from a openai interview during the phone round.

It covers the following topics: Mle, Onsite, Phone .

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Mle Onsite Phone

About OpenAI Interview Reports

This question was reported by a candidate who interviewed at OpenAI. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at OpenAI are the higher-signal extractions to take from this report.

For broader preparation context, the OpenAI interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in OpenAI reports consistently are the ones worth investing in; one-off niche problems are not.

During Your OpenAI Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in OpenAI reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.