Etsy Interview Questions
We track 2 interview questions reported from Etsy: 2 in ML Fundamentals & Algorithms. They average 3.0/5 difficulty — medium for a working engineer — and every one was reported by someone who sat the loop, not written by us. The topics that come up most: nlp, bert, transformers, pytorch.
Practice these on the problems board →Question mix
- ML Fundamentals & Algorithms2
Difficulty
- 3/5 — medium2
Asked for MLE (2), RS (2)
Topics Etsy asks about
Reported Etsy questions
Debug an NLP Classifier Training Pipeline (BERT + Linear)
3/5This Etsy interview scenario places you inside a broken machine learning pipeline combining transformer models and linear layers for text classification. You must diagnose and resolve subtle bugs spanning data loaders, training loops, and evaluation metrics to get the system operational within a constrained timeframe. The task evaluates your practical debugging instincts and familiarity with deep learning frameworks. To unlock the complete codebase analysis and model solution, you need an active subscription.
ML Fundamentals & AlgorithmsnlpberttransformersImplement Dense Layer with Numpy
3/5Understanding the mathematical mechanics behind neural network layers is essential for machine learning engineers, as explored in this interview question reported by Etsy. You are asked to manually construct a fully connected layer using NumPy, implementing both forward propagation and gradient backpropagation alongside a custom activation function. To explore the complete implementation guidelines and fully worked model solution, a paid subscription is required.
ML Fundamentals & Algorithmsnumpyneural-networkbackpropagation
Companies that ask similar questions
Etsy interview FAQ
- How many Etsy interview questions are available?
- 2 reported Etsy questions, the largest group being ML Fundamentals & Algorithms (2).
- How hard is the Etsy interview?
- Across the questions we track, Etsy averages 3.0 out of 5: 2 at 3/5.
- What topics does Etsy ask about?
- Most often nlp, bert, transformers, pytorch, debugging.