Senior Machine Learning Engineer Interview Questions

617 senior machine learning engineer interview questions shared by candidates

1. Organizzare il codice in classi seguendo principi di software design (SOLID etc) 3. Qual è il tuo modello di deep learning preferito e perché, come lo metteresti in prod 4. Cosa cambieresti del processo nella tua azienda dal punto di vista del dato
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Senior Machine Learning Engineer

Interviewed at Prima Assicurazioni

3.9
Jun 10, 2025

1. Organizzare il codice in classi seguendo principi di software design (SOLID etc) 3. Qual è il tuo modello di deep learning preferito e perché, come lo metteresti in prod 4. Cosa cambieresti del processo nella tua azienda dal punto di vista del dato

Interview with Manager / Head of department: - My background and past projects Codility Test: - If you do Codility samples, you will be good to go! Technical Interview: - System Design - Load balancing of requests - REST API design - A small coding exercise (took 10 minutes) Bar Raiser: - Very similar to Technical Interview except that I was expected to behave in a more mature (business like way) - Why do I want to join Delivery Hero
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Senior Machine Learning Engineer

Interviewed at Delivery Hero

3.5
Sep 3, 2021

Interview with Manager / Head of department: - My background and past projects Codility Test: - If you do Codility samples, you will be good to go! Technical Interview: - System Design - Load balancing of requests - REST API design - A small coding exercise (took 10 minutes) Bar Raiser: - Very similar to Technical Interview except that I was expected to behave in a more mature (business like way) - Why do I want to join Delivery Hero

Coding: 1. Linked list from scratch 2. Leetcode House robber problem Theory: LLM: Transformer architecture in depth Deep Learning: CNN, layers, activation functions, loss functions ML: Decision trees and random forest
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Senior Advisor Machine Learning Engineer

Interviewed at Dell Technologies

3.7
Oct 28, 2024

Coding: 1. Linked list from scratch 2. Leetcode House robber problem Theory: LLM: Transformer architecture in depth Deep Learning: CNN, layers, activation functions, loss functions ML: Decision trees and random forest

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