Hey,

Got rejected after Google’s apprenticeship phone interview (Software Application Development). I know I messed up –I recently interviewed for Google’s apprenticeship (Backend/ML track) but got rejected after the phone round. I suspect my answer about improving a Google product tanked me. Could you help me refine my approach to align better with Google’s interview style?

Initially HR asked me to introduce myself, so I mentioned my name, my graduation details, my technical skills, and my internships.

then she proceeded with the interview.

Q: "How would you improve a Google product?"
My answer:
"I use Google Keep for task management. We can implement an AI chatbot to add and remove tasks. Additionally, we can add a feature that automatically detects and formats any pasted code based on its programming language."

Q: "Why do you want this apprenticeship?"

My answer:
*"*I want to grow in the field of backend development, as I’m already familiar with Node.js and have built several solid projects using Node and React. I'm currently working as an ML intern, which has made me more curious and motivated to explore the field of machine learning in greater depth"

❓ Where I need help:

1. Was Google Keep a bad choice? Should I have picked a product like Cloud Functions, Firebase, or Workspace instead?
2. How technical should answers be? Should I mention tools like TensorFlow, Kubernetes, or NLP models?
3. What does Google look for in "product improvement" answers? Scalability? User impact? Technical novelty?
4. Any tips from Googlers/apprentices on structuring answers?

My background:

• B.Tech ECE grad w/ internships in Node.js/React, AWS, and ML.
• Projects: Built [1-sentence description, e.g., "finance dashboard with ML forecasting", "food ordering and recipe web app", "Excalidraw Clone Application with Share Functionality for Collaborative Drawing"].

Rejections sting, but I’m determined to learn! Thanks for any advice—especially from those familiar with Google’s hiring process.

🙏 Asking You:

1. How would YOU have answered these?
2. Was Google Keep a terrible choice? What product would’ve been smarter for backend/ML?
3. Resources for practicing product-focused interviews?