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Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Check depth in Kotlin coroutines, Jetpack Compose versus XML views, lifecycle handling, Room, Hilt or Dagger, and Gradle build variants; ask what they debugged with Android Studio Profiler.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names concrete APIs and libraries, explains coroutine scoping and recomposition pitfalls, and shows fluency with Gradle modules and dependency injection.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe architecture choices: MVVM or MVI, offline-first sync, WorkManager scheduling, minSdk trade-offs, and how they cut APK size, cold start time, or battery drain.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Justifies architecture against real constraints like fragmentation, background limits, and memory, citing measured before and after numbers.
03
Evaluation factor
Evidence and rigour
25% weight
Assess how they verify releases: Espresso and JUnit coverage, Firebase Crashlytics triage, ANR rates, staged Play Console rollouts, and evidence behind a rollback decision.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Quotes crash-free session rates, ANR thresholds, and uses instrumentation plus phased rollout data before declaring a build healthy.
04
Evaluation factor
Collaboration and communication
15% weight
Look for how they work with iOS peers, backend teams on API contracts, and designers on Material 3 specs; ask about a code review disagreement they resolved.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes negotiating API payloads and design fidelity concretely, gives reviewable feedback, and documents decisions the rest of the team can follow.
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