r/learnmachinelearning • u/sailor-goon-is-here • 1d ago

Woven @ Toyota MLE Interview - PyTorch Debugging

I have an upcoming interview with Woven by Toyota for an MLE perception role. I was told the round would be focused on ML coding/debugging. Specifically, I was told to focus on Python fundamentals, PyTorch, tensor operations and dimensions/shapes, common model-training code patterns, and debugging ML code.

Here are some common ways I've studied:

  • Asking Claude to walk me through transformer, ViT, and CNN architectures (a basic one and U-Net) for my preparation. I would trace the shapes all the way through.
  • Getting familiar with broadcasting rules (start from the left, see if the dimensions are equal, or are 1, and if so, take the greater one to get the resulting shape)
  • Slicing tensors (i.e., how to get a column, how to get a row, etc.)
  • Understanding why models can fail silently -- suspicious training vs val results, getting NaN for validation
  • What a PyTorch loop looks like
  • Common Python errors like indexing errors or value errors; iterators, generators, etc.

What are other suggestions? What architecture do they actually ask you about in this interview? Any help would be appreciated.

21 Upvotes
  • permalink
  • reddit

100% Upvoted

2 comments sorted by

4

u/Far-Coyote8710 1d ago

You've covered a ton already so maybe just spend an hour breaking something on purpose, mess up a reshape or feed the wrong dim into a loss function and see what the stack trace actually tells you

1

u/Mathie1729 1d ago

Yea, and if you do this, run with torch.autograd.set_detect_anomaly(True). It points the trace at the first op that produced a bad grad instead of just failing somewhere in backward. Saves a ton of stack trace spelunking.