AI Research Scientist · Full Journey · Multiple Types

Google — AI Research Scientist ✅ Passed

Level: Intern

Round: Full Journey · Type: Multiple Types · Difficulty: 6/10 · Duration: 120 min · Interviewer: Unfriendly

Topics: Behavioral Questions, Machine Learning Fundamentals, LLM, Generative AI, Fine-tuning, Autoencoder, VAE, VQ-VAE

Location: Mountain View, CA

Interview date: 2026-01-20

Summary

Round 1: Behavioral (BQ)

Question: Common behavioral questions about the most interesting project, working with different people, learning from different backgrounds, helping others, and dealing with challenges.

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Round 2: Machine Learning (ML)

Question: After a 10-minute resume review, I was asked machine learning fundamentals questions focused on LLMs and generative AI. The interviewer asked about fine-tuning methods, their advantages and disadvantages, and scenario-based questions. For generative AI, the interviewer asked about autoencoders, VAE, and VQ-VAE.

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Details

Preparation Tips & Key Takeaways

What I Learned

• I needed a stronger understanding of LLM fine-tuning techniques and their trade-offs.
• Reviewing the fundamentals of autoencoders, VAEs, and VQ-VAEs is crucial for generative AI roles.

Recommended Preparation

Behavioral Questions

• Prepare STAR stories that highlight my project experience.
• Think about scenarios where I've worked with diverse teams.

Machine Learning

• Deeply understand LLM fine-tuning methods and their pros and cons.
• Review the theory and application of generative models like autoencoders, VAEs, and VQ-VAEs.

Resources I Recommend

• Standard machine learning textbooks covering generative models.
• Online courses and research papers on LLM fine-tuning.

Common Pitfalls to Avoid

• Being too general in behavioral answers; use concrete examples.
• Lacking a detailed understanding of the mathematical foundations of generative models.