Scale.ai·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Scale.ai ML Engineer coding round where they drop you into a broken training script and expect you to debug it in under an hour. Four bugs, all small, but reading someone else's ML code under time pressure is a different skill than writing your own.

Questions Asked (1)

You are given a Python ML training script with 3 to 4 bugs. Each bug can be fixed in about 1 to 2 lines. Run the script, read the errors and outputs, identify each bug, and patch the code so training runs correctly. You have 60 minutes.

Technical Trade-offsRoot Cause AnalysisAlgorithms & Data Structures

Author's notes

The bugs themselves weren't that hard once I found them.

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AI HintsAI Generated

Suggested Approach

First, run the script to observe the actual error and traceback, then systematically debug each issue by reading the error messages and inspecting the relevant code sections. Fix one bug at a time, re-running the script after each fix to confirm progress and avoid introducing new errors. Prioritize bugs that block execution (e.g., syntax, shape mismatches) before those affecting performance or correctness.

Pro tip: Use print statements or a debugger to inspect tensor shapes and data types at key points; many ML bugs stem from shape mismatches or incorrect data preprocessing. Also, keep track of time—if stuck on one bug for more than 10 minutes, move to the next and return later with fresh eyes.

1. Run and Observe

Execute the script and carefully read the error message and traceback to identify the first failure point. Note any warnings or unexpected outputs that might hint at other bugs.

2. Isolate and Diagnose

For each error, locate the relevant code lines and reason about the root cause. Check for common ML bugs: shape mismatches, incorrect loss functions, data leakage, or wrong hyperparameters.

3. Patch and Verify

Make minimal fixes (1-2 lines) and re-run the script to confirm the error is resolved. Ensure the fix doesn't break other parts of the code.

4. Iterate and Validate

Repeat steps 1-3 for each bug until the script runs without errors. Finally, validate that training completes and metrics (e.g., loss decreases) are reasonable.

Key Points to Mention

Systematic debugging: using error messages and tracebacks to guide fixes.
Common ML bugs: shape mismatches, incorrect loss functions, data preprocessing errors, and device placement (CPU/GPU).
Incremental testing: re-running after each fix to ensure correctness and avoid compounding errors.
Time management: prioritizing bugs that block execution and setting time limits per bug.
Code inspection: checking tensor shapes, data types, and model architecture for inconsistencies.
Validation: ensuring the final model trains and converges, not just runs without errors.

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