“You are not scored on your answers.
You are scored on the signals you emit while answering.”
This document describes the evaluation dimensions used internally by Hiring Committees (HC) during Google SRE interviews.
It is not a checklist. It is a map of attention — what interviewers are trained to notice while you speak, reason, and react under pressure.
Most candidates fail because they optimize for correctness. Successful candidates optimize for trust.
Interviewers do not grade you on a scale of 1 to 10 for “knowing Linux.” They write a narrative justification for a Hire or No Hire recommendation, backed by specific signals.
Here is the difference between what you say and what they write:
- Candidate: "I will check the database CPU and query logs."
- Interviewer Note: "Candidate jumped straight to RCA (Root Cause Analysis). Failed to check user impact or stabilize the system. Weak operational judgment."
+ Candidate: "I'll drain traffic from the failing region to stop user impact, then I'll investigate the database."
+ Interviewer Note: "Candidate demonstrated excellent execution sequencing. Prioritized mitigation and blast-radius containment before debugging. Strong Hire."
The Hiring Committee does not re-evaluate your technical answers. They evaluate the patterns across these scorecards. One strong negative signal (like ignoring user impact) repeated across two rounds is usually decisive.
Every Google SRE interview — regardless of the round — rolls up into five evaluation dimensions.
Can this person make safe decisions under ambiguity?
Does the candidate do the right things in the right order?
Does the candidate understand where failures actually live?
Can this person be trusted to lead a War Room in a real incident?
Does this person think in terms of risk, not features?
Candidates who are rejected often receive feedback like:
“Strong technical skills, but concerns around execution.”
This maps directly to these scorecard dimensions. You solved the LeetCode problem, but your code wasn’t safe for production. You designed the system, but your execution sequence was backwards.
Because interviewers are explicitly trained not to share this level of detail with candidates, the feedback feels vague. This document exists to make that gap visible.
Understanding scorecards improves your awareness. It does not improve your performance.
Under the pressure of a 45-minute Google interview:
Candidates rarely fail on one mistake. They fail due to signal drift they never notice during the interview.
Strong candidates don’t just read the rubric; they practice against it. They use:
Only repeated simulation builds the reflexes these scorecards reward.
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This file reveals the shape of the evaluation.
The full system trains you to score ‘Exceptional’ inside it.