Describe a failure and a success

Questions

1. Describe an occasion when you failed (or when things did not unfold as intended). What took place, what did you take away from it, and what would you change next time?
2. Describe an occasion when you were successful. What exactly did you contribute, and what effect did it have?

Expectations

• Draw on specific examples from engineering or ML work, such as delivery, incidents, model launches, or collaboration.
• Emphasize ownership, communication, and outcomes that can be measured.
• Add reflection on what you changed afterward. Overview: This question assesses ownership, communication, accountability, and how you evaluate impact in engineering and machine learning settings, with attention to incident management, model launches, delivery results, and collaboration. Solution

Use a clear framework: STAR or CAR

One dependable format is STAR:

• Situation: the context and what was at stake
• Task: your own responsibility, not the team's
• Action: what you did, the decisions you made, and the tradeoffs involved
• Result: a measurable outcome plus what you learned Alternatively, use CAR (Context–Action–Result) when you want something shorter.

1) Failure story: what interviewers are looking for

They are looking for:

• An actual failure, not a disguised humblebrag
• Taking ownership without spiraling into self-blame
• Concrete corrective steps and a lasting change to the process

Strong failure examples (choose one)

• Released a model without adequate offline/online parity checks, and online metrics regressed.
• Underestimated data quality problems, so the training pipeline silently produced corrupted data.
• Misaligned with stakeholders, leading to building the wrong thing or scoping the MVP incorrectly.

What to cover

• Your decision point: why you selected that path given the information available at the time.
• Detection and response: how you triaged the issue, communicated it, and reduced the damage.
• Prevention: what you changed afterward, such as tests, monitoring, review checklists, or rollout plans. Specific details that strengthen the story:
• Include numbers: “CTR dropped 1.2% relative,” “p95 latency +40ms,” or “2-hour incident.”
• Mention a process fix: canary releases, automated data validation, feature store adoption, or a postmortem template.

2) Success story: what interviewers are looking for

They are looking for:

• A clear individual contribution and leadership, even without formal authority
• Impact shown through both business and technical metrics
• Sound decision-making around tradeoffs, prioritization, and execution

Strong success examples

• Launched a ranking model or retrieval improvement that produced a measurable lift.
• Cut training cost or latency substantially.
• Created an experimentation framework or logging pipeline that unblocked several teams.

What to cover

• Scope: why the work mattered.
• Your role: design choices, building alignment, and removing blockers for others.
• Outcome: A/B results, reliability gains, or developer productivity improvements.

3) Handling follow-up questions

Common follow-ups and ways to respond:

• “What would you do differently?” → name one or two specific changes you now put into practice.
• “What did you learn?” → connect it to a principle, such as “validate assumptions early” or “instrument before optimizing.”
• “How did you influence others?” → point to your communication rhythm, written docs, and design reviews.

4) Quick prep checklist to avoid rambling

Prepare two stories, one failure and one success, each with:

• A one-sentence summary
• Three actions you personally took
• Two metrics showing impact
• One lasting change you put in place This reliably leads to senior-level behavioral answers.