Leading engineering teams

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Hiring, coaching, performance, team design and delivery for teams that build AI.

On this page: Both tracks (1) · AI Leadership (20)

Both tracks

• Describe how you identified a valuable opportunity and persuaded a group to deliver it.
  • Behavioral · Senior · Asked at: Monzo ✅ · ✍ Answer · Suggest a fix
  • Tests: Whether you create influence through evidence, alignment and shared ownership.
  • A strong answer covers:
    • Explain why the opportunity mattered and how you discovered it.
    • Describe competing priorities, objections and how participants shaped the plan.
    • Show delivery and impact, including what remained outside your control.
  • Read: Design Docs at Google (Industrial Empathy) · Site Reliability Engineering (Google)

AI Leadership

• How would you handle an engineer who is not meeting performance expectations?
  • Applied scenario · Senior · Asked at: Google and Google DeepMind 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you diagnose a performance gap, provide fair support and make expectations and follow-up explicit.
  • A strong answer covers:
    • Establish observable expectations and examples of the gap; check role clarity and working conditions.
    • Hear the engineer’s account, agree support and define measurable improvement with review dates.
    • Follow the organisation’s people process, distinguish improvement from reassignment or exit, and protect confidentiality.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• How would you handle a high-performing engineer whose behaviour repeatedly creates conflict with teammates?
  • Applied scenario · Senior · Asked at: Google and Google DeepMind 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you address observable collaboration problems without exempting high performers or deciding the case before hearing those involved.
  • A strong answer covers:
    • Establish specific behaviour and team impact; hear different accounts without public blame.
    • Explain that collaboration is part of performance and agree observable changes and support.
    • Review both delivery and collaboration; use the established people process if harmful behaviour persists.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• Describe how someone you mentored progressed in their career.
  • Behavioral · Senior · Asked at: Anthropic 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you develop others through opportunities and evidence rather than taking credit for their success.
  • A strong answer covers:
    • Describe their development goal and starting point.
    • Explain how coaching helped them choose actions.
    • Distinguish their achievements from your support.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• What do you do when your team disagrees with your proposed direction?
  • Applied scenario · Senior · Asked at: Anthropic 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you invite challenge while retaining responsibility for a decision.
  • A strong answer covers:
    • Clarify the disputed assumptions and actively seek the strongest objection.
    • Use evidence or a bounded experiment where it can resolve the disagreement.
    • Explain decision rights, communicate the choice and define when to revisit it.
  • Read: Design Docs at Google (Industrial Empathy)
• Tell me about a difficult organisational change you managed that involved high-performing engineers leaving. How did you respond?
  • Behavioral · Senior · Asked at: Google and Google DeepMind 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you can explain actual attrition, your responsibility and the effect of your response on the remaining team.
  • A strong answer covers:
    • Use a real change and actual departures; state your authority and distinguish known reasons from inference.
    • Explain how remaining engineers reacted and what you changed in workload, communication or management practice.
    • Give follow-up evidence, limits of attribution and what you would handle differently.
  • Read: Organizational Change Management (GitLab Handbook)
• How would you stabilise an engineering team after a change in management?
  • Applied scenario · Senior · Asked at: Google and Google DeepMind 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you establish trust and operating clarity before imposing broad changes.
  • A strong answer covers:
    • Listen to team members and partners and identify urgent delivery or wellbeing risks.
    • Clarify priorities, decision ownership and communication routines.
    • Make a small number of justified changes and check their effect.
  • Read: Organizational Change Management (GitLab Handbook)
• What is the largest team you have managed, and how did your responsibilities change at that scale?
  • Self-presentation · Senior · Asked at: Anthropic 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether your management model matches the actual span and complexity.
  • A strong answer covers:
    • State the size precisely: direct reports, managers beneath you, total headcount and what the team owned.
    • Explain what you stopped doing personally and which mechanisms replaced your direct involvement.
    • Close with a signal that showed whether the model worked, and what you changed in response.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• How have you managed and developed your strongest performers?
  • Behavioral · Senior · Asked at: Meta 🗣 · Suggest a fix
  • Tests: Whether you provide challenge and growth without creating a privileged exception.
  • A strong answer covers:
    • Start from what each strong performer wants next, and stretch them with harder problems, not more volume.
    • Avoid a privileged exception: same standards on collaboration, and stretch work offered openly, not by favour.
    • Close with evidence of their growth, such as scope they now own alone, and whether they stayed.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• A cross-functional partner gives you critical feedback about your leadership. How do you respond?
  • Applied scenario · Senior · Asked at: Meta 🗣 · Suggest a fix
  • Tests: Whether you investigate feedback and translate it into changed behaviour.
  • A strong answer covers:
    • Ask for specific instances and their impact on the partner's team, holding back any defence at first.
    • Test whether it is a pattern with other partners and your team, separating your behaviour from structural friction.
    • Commit to one visible change, tell the partner what it is, and ask them later whether it worked.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• Describe a team disagreement that you resolved without discarding the valid concerns on either side.
  • Behavioral · Senior · Asked at: Google and Google DeepMind 🗣 · ✍ Answer · Suggest a fix
  • Tests: Whether you uncover underlying needs instead of forcing a superficial compromise.
  • A strong answer covers:
    • Pick a disagreement where both positions protected something real, and state each in its strongest form.
    • Show how you moved from positions to the needs behind them and found an option meeting both.
    • Close with what was decided, whether both concerns stayed addressed, and how you checked.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• Describe your most recent strategic engineering hire. How did you define the hiring bar and assess whether the hire met the need?
  • Behavioral · Senior · Asked at: Stripe † · ✍ Answer · Suggest a fix
  • Tests: Whether a real hiring decision connects a capability need, a consistent assessment and observed results.
  • A strong answer covers:
    • State the real strategic need, your role in the decision and why hiring was chosen.
    • Explain the role-specific bar, assessment evidence and important tradeoffs.
    • Describe onboarding and observed results; distinguish early signals from a proven long-term outcome.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• How do you establish engineering quality standards across a team?
  • Applied scenario · Senior · Asked at: Stripe † · ✍ Answer · Suggest a fix
  • Tests: Whether standards are observable, proportionate and part of daily delivery.
  • A strong answer covers:
    • Define quality by observable outcomes, not by taste or a document nobody reads.
    • Scale the requirement to the risk of the change, so low-risk work is not slowed.
    • Put the standard in the default path — tooling, templates, CI — and revisit it with data.
  • Read: Site Reliability Engineering (Google)
• How would you convince engineers that a proposed product feature is worth building?
  • Applied scenario · Senior · Asked at: Perplexity † · Suggest a fix
  • Tests: Whether you engage technical concerns and establish a shared reason to invest.
  • A strong answer covers:
    • Bring the user problem and the evidence behind it, not the feature, so engineers can judge the reasoning.
    • Treat objections about cost, complexity or maintenance as inputs that can reshape scope, not as resistance.
    • Agree a success metric and a cheap first version, and commit to cutting it if the metric misses.
  • Read: User Needs + Defining Success (Google PAIR) · Design Docs at Google (Industrial Empathy)
• What have you changed about your management approach as you have grown?
  • Behavioral · Senior · Asked at: Meta 🗣 · Suggest a fix
  • Tests: Whether reflection produces concrete changes in how you lead.
  • A strong answer covers:
    • Name one or two specific practices you changed, not a general shift in philosophy.
    • Explain what triggered each change, such as feedback or a team outcome, and what you believed before.
    • Show the effect as your team would describe it, and name what you are still changing.
  • Read: [re:Work] Google's New Manager Training Slides (Google re:Work)
• Researchers want more exploration while product engineers need a reliable release. How would you structure ownership and handoffs?
  • Applied scenario · Senior · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether team design accommodates uncertainty without abandoning production ownership.
  • A strong answer covers:
    • Separate what is being explored from what is being operated, and give each a different bar.
    • Define the handoff as a contract: an interface, evaluation evidence and an owner on call.
    • Protect the release with versioning and a rollback path, so exploration cannot destabilise it.
  • Read: Rules of Machine Learning (Google for Developers)
• An engineering team adopts AI coding tools, but reviewers struggle to verify the resulting changes. How would you build the missing capability?
  • Applied scenario · Senior · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether enablement is tied to verification and ownership rather than tool usage counts.
  • A strong answer covers:
    • Name the real gap: reviewing a large change nobody drafted by hand, not tool operation.
    • Keep the author accountable for explaining and testing the change, whatever produced it.
    • Train review on verification habits and measure escaped defects, not adoption counts.
  • Read: Designing AI resistant technical evaluations (Anthropic)
• Two product teams need shared AI infrastructure, but neither can spare an engineer. How would you establish ownership without creating an unstaffed platform promise?
  • Applied scenario · Senior · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether you resolve capacity and accountability before creating a new team boundary.
  • A strong answer covers:
    • Define the shared need precisely and compare its cost with each team building its own.
    • Choose a staffing model explicitly: funded headcount, capacity taken from both roadmaps, or one owning team.
    • Write a narrow charter with a named owner, support terms and the trigger for a dedicated team.
  • Read: Team Topologies (Martin Fowler)
• Candidates now use AI assistants during technical interviews. How would you redesign your loop so that it still produces a usable hiring signal?
  • Applied scenario · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether you define the hiring signal, set a clear tool policy and evaluate individual judgement consistently.
  • A strong answer covers:
    • Define the job capabilities and identify tasks whose score no longer distinguishes candidates.
    • Choose and disclose a tool policy for each stage; give candidates comparable access and evaluate verification and explanation.
    • Calibrate on representative work, review scoring disagreements and examine later performance without overclaiming small samples.
  • Read: Designing AI resistant technical evaluations (Anthropic) · AI Interview Success: An Interviewer's Inside Guide (Canva Engineering Blog)
• You manage three engineering managers delivering a shared AI initiative. Two blame each other for an evaluation gap while the executive sponsor has promised a launch date. How would you restore accountability without taking over their teams?
  • Applied scenario · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether you lead through managers, resolve cross-team decision rights and negotiate executive commitments using evidence.
  • A strong answer covers:
    • Establish the missing outcome, prior agreements and each manager’s account; separate unclear structure from individual avoidance.
    • Assign an accountable decision owner and interfaces while managers retain staffing and delivery responsibility.
    • Agree evidence-based options with the sponsor, coach managers privately and review whether the organisation can resolve the next conflict without you.
  • Read: Director, Engineering (GitLab Handbook) · Communication (GitLab Handbook) · Team Topologies (Martin Fowler)
• Your organisation adopted AI coding tools and leadership wants to know whether they worked. How would you answer that credibly?
  • Applied scenario · 🧪 generated from job-posting themes · ✍ Answer · Suggest a fix
  • Tests: Whether you choose a measure that survives scrutiny and admit what the available data cannot settle.
  • A strong answer covers:
    • Make the claim precise first — faster delivery, more change, better quality or less toil — because each needs different evidence and they can move in opposite directions.
    • Prefer outcomes a customer feels, such as delivery lead time and change failure rate, over activity counts like accepted suggestions, which rise when the tool is used rather than when the work improves.
    • Compare against something — a staged rollout, comparable teams, or the same teams before and after — and name the confounders you could not remove.
  • Read: Patterns of Trustworthy Experimentation: Pre-Experiment Stage (Microsoft Research) · Rules of Machine Learning (Google for Developers)

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