Openai·Machine Learning Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Technical screen for an MLE role at OpenAI. One long, dense debugging question covering basically every failure mode in a PyTorch transformer pipeline at once. Felt more like a written exam than a conversation.

Questions Asked (1)

You're given a broken PyTorch transformer training pipeline (encoder-decoder with multi-head attention, AdamW, AMP, gradient accumulation, cosine LR with warmup, teacher forcing) that shows training loss diverging, validation accuracy stuck near chance, intermittent AMP crashes, and nondeterministic results across runs. Identify at least four root-cause bugs, explain the failure mode for each, show a minimal fix, and write a test or assertion that would catch each one. Then lay out a systematic debugging plan covering data preprocessing, masking, positional encodings, loss computation, optimizer state, AMP/GradScaler, seed control, and DDP config. Finally, describe your sanity checks, which metrics you'd monitor, and a small experiment to verify each fix.

Root Cause AnalysisSystem DesignTechnical Trade-offs

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a systematic debugging exercise: reproduce the issues, isolate each component, and verify fixes with targeted tests. Prioritize root causes that explain multiple symptoms (e.g., masking bug causing divergence and stuck validation), then address AMP and determinism separately. Structure your answer around a clear debugging plan and concrete fixes with assertions.

Pro tip: Demonstrate production maturity by emphasizing reproducibility first: set all seeds, enable deterministic algorithms, and log everything. Then, use a minimal synthetic dataset to quickly validate fixes before scaling up.

1. Reproduce and Baseline

Create a minimal reproducible script with fixed seeds, small synthetic data, and logging of loss, gradients, and AMP scale. Confirm the reported symptoms (divergence, stuck validation, AMP crashes, nondeterminism).

2. Identify Root Causes

Systematically inspect each component: data preprocessing, masking, positional encodings, loss computation, optimizer state, AMP/GradScaler, seed control, and DDP config. For each bug, explain the failure mode and propose a minimal fix.

3. Write Assertions and Tests

For each identified bug, write a unit test or runtime assertion that would catch it (e.g., check mask shapes, verify loss decreases on a tiny batch, assert deterministic outputs across runs).

4. Systematic Debugging Plan

Outline a step-by-step plan: validate data pipeline (shapes, tokenization, padding), check masking logic (causal and padding masks), verify positional encoding implementation, inspect loss (ignore_index, reduction), review optimizer and scheduler states, test AMP with/without GradScaler, enforce seeds, and verify DDP setup.

5. Sanity Checks and Monitoring

Describe sanity checks: overfit a single batch, check gradient norms, monitor AMP scale factor, track loss and accuracy on a small validation set. Propose small experiments to verify each fix (e.g., disable AMP to see if crashes stop).

Key Points to Mention

Masking bugs: incorrect causal mask or padding mask causing attention to future tokens or padding, leading to divergence and stuck validation.
AMP issues: missing GradScaler or incorrect scaling causing intermittent crashes; ensure scaler is used correctly and check for inf/nan.
Determinism: set seeds for Python, NumPy, PyTorch, and CUDA; enable torch.use_deterministic_algorithms; set CUBLAS_WORKSPACE_CONFIG.
Optimizer and scheduler: verify AdamW weight decay excludes biases and LayerNorm; ensure scheduler steps correctly with gradient accumulation.
Loss computation: check ignore_index for padding, reduction, and that loss is computed on shifted targets for teacher forcing.
DDP configuration: ensure proper initialization, find_unused_parameters, and that seeds are set per process.

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