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 Laravel internals: Eloquent relationships and N+1 avoidance, service container bindings, queues with Horizon, middleware, Blade versus Inertia or Livewire, and Composer dependency handling.
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 specific Laravel versions and upgrade paths, explains container bindings and eager loading precisely, and writes Pest or PHPUnit tests routinely.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe database and architecture choices: MySQL indexing and migration strategy, Redis caching, job queue design, multi-tenancy, API versioning, and when they avoided a package rather than adding one.
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
Walks through a concrete scaling decision, quantifies query or response time gains, and states the trade-off they accepted knowingly.
03
Evaluation factor
Evidence and rigour
25% weight
Test how they diagnose production issues using Telescope, Sentry, slow query logs or Laravel logs, and how they verify a fix beyond it working locally.
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
Describes a real bug traced through logs and profiling, with a regression test added and deploy behaviour confirmed afterwards.
04
Evaluation factor
Collaboration and communication
15% weight
Assess handoffs with designers and frontend developers, pull request review habits, ticket hygiene in Jira or Linear, and how they explain schema changes to non-technical stakeholders.
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
Gives examples of review comments that changed a design, documents endpoints clearly, and pushes back on vague requirements before building.
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