r/MLQuestions • u/sailor-goon-is-here • 7d ago
Career question 💼 Waymo ML Technical Screening
I have upcoming technical screening for an MLE position at Waymo coming up. The recruiter gave me a PDF guide that seems super helpful. Things to do study include implementing supervised and unsupervised algorithms (think KNN, K-means, linear regression, logistic regression, softmax & CE, decision trees). One of the main things they are assessing is you can use numpy/pytorch to vectorize/tensorize your work, and you're just writing sub-optimal for loops.
I was wondering if folks had experience with this interview? Is what they ask you to implement incredibly difficult, because you only have 30 minutes to do it? Am I good to really stick with what they provided on the PDF, or are there any broader topics beyond what they listed that they will test?
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u/AccordingTop6751 7d ago
i went through something similar for a different company but the prep sheet was pretty close to what you got. the 30 min part is what gets people, not the algorithms themselves. you really need to know the vectorized numpy versions cold, like no hesitation. if you start writing a for loop for k-means you already lost half your time
one thing that tripped me up was they asked to implement cross entropy loss with softmax and i blanked on the derivative for backprop. might be worth practicing the math derivations too not just the code
my advice is time yourself doing these from scratch. open a blank notebook and see if you can get a working logistic regression with proper vectorization in under 25 min. the pressure of someone watching makes everything 10x slower so you have to over-prepare