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Openai·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

OpenAI SWE interview with a PyTorch debugging and implementation problem. Pretty hands-on, they gave you a spec and expected working code plus the ability to spot classic training loop bugs.

Questions Asked (1)

Q1

Implement a 2-layer MLP in PyTorch for binary classification, write a full training loop, and fix any bugs in provided code (e.g. missing zero_grad, shape mismatches, wrong loss function usage).

Algorithms & Data StructuresTechnical Trade-offsRoot Cause Analysis
Author's notes

This was more involved than I expected for a single question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the task and then walk through the MLP architecture, training loop, and common bugs systematically. For each bug, explain why it's wrong and how to fix it, emphasizing shape consistency and correct loss usage. Conclude by discussing potential improvements and trade-offs.

Pro tip: Always verify tensor shapes at each step and use assertions or print statements during debugging; this demonstrates rigorous engineering and prevents silent errors.

1. Clarify requirements and assumptions

Confirm input dimensions, output format (logits vs probabilities), and dataset characteristics. Ask about expected performance and constraints.

2. Design the MLP architecture

Define a 2-layer MLP with appropriate activation (e.g., ReLU) and output layer (single logit for binary classification). Ensure input and hidden dimensions are consistent.

3. Write the training loop

Include forward pass, loss computation (BCEWithLogitsLoss), zeroing gradients, backward pass, and optimizer step. Add validation and logging.

4. Identify and fix bugs

Check for missing zero_grad, shape mismatches (e.g., target shape), incorrect loss function (e.g., using CrossEntropyLoss for binary), and device mismatches.

5. Discuss trade-offs and improvements

Mention alternative loss functions, optimizers, regularization, and how to scale to multi-class or larger networks.

Key Points to Mention

  • Use BCEWithLogitsLoss for numerical stability and avoid applying sigmoid before loss.
  • Ensure target tensors have shape (batch_size, 1) or (batch_size,) and match output shape.
  • Always call optimizer.zero_grad() before backward pass to prevent gradient accumulation.
  • Verify tensor shapes at each step, especially after linear layers and loss computation.
  • Consider using torch.nn.Sequential for cleaner architecture definition.
  • Discuss trade-offs between different activation functions and optimizers (e.g., Adam vs SGD).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.