Google | Hiring Manager Round | Gemini Applications | SWE III ML | Bengaluru
Anonymous User
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GoogleGeminiData ScienceMachine Learning EngineerMachine LearningBengaluruInterviewData Scientist / Data Engineer

Google Hiring Manager Round (Gemini Applications Team)

Recently, I gave the Hiring Manager round for SWE III (ML) at Google for the Gemini Applications team.
I have ~4.5 years of experience as a Data Scientist in a mid-size company.
The interview lasted around 30 minutes and focused on introduction followed by questions around building and deploying scalable systems.

Interview Questions & Discussion

Q1. Can you introduce yourself and walk me through one project?

My Answer:
Introduction and brief discussion about one project

Q2. How is evaluation done for such systems?

My Answer:
Answered relevant to the project

Q3. How would you design a multi-task system that handles varying domains?

My Answer:
Given constraints like limited resources and business pressure, and assuming access to a new model from the Gemini team:

  • Use golden datasets to evaluate performance across different tasks
  • Apply prompt enhancements instead of fine-tuning due to time constraints
  • Highlighted that a single model may not effectively handle all domains

Q4. How would you develop or improve the model over time?

My Answer:
With the help of golden datasets and stakeholders

Q5. What would you do if no additional data is available?

My Answer:
Could not give a satisfactory answer
(Expected: agentic-based evaluation on top of golden datasets to iterate faster)

My Questions to the Interviewer

Q1. How do you ensure systems are reliable across so many domains?

Q2. Do you use internal frameworks, or is there a dedicated team building solutions for new agentic workflows while others focus on implementation?

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Google

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