Machine Learning Engineer
Tell me about a time you disagreed with a data scientist or product manager about whether a model was ready to ship.
What the interviewer is looking for
- This question tests cross-functional collaboration. Works through disagreements with scientists and product managers using evidence and agreed launch criteria.
- What to bring out: Uses evidence and agreed launch criteria, proposes options like a limited rollout, and reaches a decision respectfully.
- Expect a follow-up like: How do you agree on launch criteria before the next project?
Another way it may be asked: Imagine product wants to launch next week but you think the model isn't reliable enough. How did you or would you handle it?
A structure to follow: STAR, with the weight on Action and Result
- Step 1
Situation
One or two sentences: where, when and what was at stake. Pick a real example you were close to.
- Step 2
Task
What you personally were responsible for. Say "I", not only "we", so the cross-functional collaboration you showed is visible.
- Step 3
Action
The two or three things you did and why you chose them over the alternatives. This should be about half your answer.
- Step 4
Result
What changed, in your own measure of it, and what you would do differently. Only use numbers you can stand behind.
This is guidance on shape, not a script. Use your own real experience and facts; never invent details to fill a gap. See the STAR guide and answer frameworks.
Common slips
- Describing what the team did without saying what you did.
- Choosing an example with no clear outcome, or one you cannot answer detailed questions about.
- Ending on the action and skipping the result and what you learned.
How to practise it
Write two bullet points for each STAR letter, then say the answer aloud in about two minutes.
Related questions
- Why do we split data into training, validation and test sets, and what goes wrong if information leaks between them?
- How would you turn a notebook a data scientist gave you into code that's safe to run in production?
- What does it mean for features to be computed the same way in training and in production, and why does that matter?
- Walk me through how you've deployed a model. How was it packaged, served and rolled out safely?
- Tell me about a time a model's performance dropped in production. How did you notice and what did you do?