You come across as thoughtful, honest, and mission-driven, with clear articulation of your team’s work and your own motivations (including why you’re exploring opportunities like OpenAI). Your time management is solid, and your stories are already structured enough that only light refinement is needed to meet a strong Senior bar.
Question 1 – Introduction / Team Vision / Success Metrics / Next Step in Career
What went well:
- You introduced your organization, tenure, past experience, team name, and responsibility in a very clear structure.
- You effectively linked your team’s work to concrete user use-cases, grounding the audience in where your org sits in the dispute flow.
- You stated your vision and mission up front, which is exactly what senior-level interviewers look for.
- You were candid about your transition from Meta to Chime, including the need to fight for bigger scope and why you’re now looking at OpenAI.
- Your OpenAI motivation was strong and multi-dimensional: mission, adoption, daily impact, frontier work in safety/finance, and global reach vs. a smaller fintech.
- You clearly explained how customer happiness and efficiency metrics are used as scorecards in your current org and how your team fits into the broader ecosystem.
- The answer was well-timed—not too long, not too dry, with enough examples.
Improvement Areas:
- For this question, there’s essentially no major gap. You can keep roughly the same structure, just polish wording as you rehearse.
Question 2 – Biggest Failure / Systematic Solution
What went well:
- You honestly owned the failure, clearly indicating your own accountability.
- The situation and context were described clearly.
- You clearly stated the resulting metric impact, making it easy for an interviewer to understand the severity.
- You shared concrete learnings, especially about over-featuring and being more careful about assumptions.
Improvement Areas:
- When describing mistakes or incorrect assumptions, try to shift from “we” to “I” (e.g., “I made an assumption that…”). This better demonstrates ownership and reflection.
- Strengthen the systematic angle: talk about how you would document the learnings, update playbooks, or change team processes so others can adopt and benefit from the lesson.
- Instead of “we asked user researchers after the fact,” frame it as planned collaboration: show that in future you would bring user research in early in the planning phase as a deliberate strategy, not just an ad hoc fix.
Question 3 – Handling Negative Feedback from Your Manager / Going Deeper
What went well:
- You set up a clear situation: limited scope vs. manager expectation of broader ownership (e.g., being responsible for a bigger chunk of the roadmap).
- You described concrete actions: reading other projects, understanding documentation, and mapping out more of the roadmap.
- You gave a tangible example of re-scoping work, such as prioritizing large volumes of upcoming disputes with queue prioritization for ops agents.
- You appeared open to feedback and willing to adjust.
- You referenced ongoing growth work (e.g., tracking actions in your growth document).
Improvement Areas:
- Add more specifics on what you proposed back to your manager. For example:
- What alternative roadmap or structure did you suggest?
- How did you reframe your scope to better match expectations?
- Include what happened after:
- Did your manager agree?
- Did you schedule follow-ups or check-ins?
- How did things look 1–2 quarters later?
This helps show that you don’t just accept feedback, but also drive and track the turnaround.
Question 4 – Views on AI Safety / Future of AI
What went well:
- You already have rich thoughts on AI safety, especially around minimizing hallucinations and harmful behavior.
- You demonstrated good awareness that reducing harm is a long-term, ongoing process, not a one-time fix.
Improvement Areas:
- Explicitly call out that AGI and frontier models will continually introduce new, never-before-seen failure modes. Safety is not “done”; it’s continuous, evolving work.
- You can strengthen this answer by linking to concrete mechanisms (e.g., red-teaming, evals, alignment research, post-deployment monitoring) and how you, as an engineering leader, would collaborate with safety teams.
- As a reference point for language and framing, I recommend reading OpenAI’s article:
