Evaluation
Evaluate how candidates write, debug and explain software. Use role-relevant tasks to examine engineering judgment, including how they review AI-assisted work.


Choose tasks aligned with the role’s engineering needs.
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Review implementation choices and problem-solving.

Explore trade-offs and technical constraints.

Assess how candidates verify AI-generated output.

Keep the submitted work alongside the evaluation criteria.

Give panelists evidence and targeted follow-up questions.

Technical assessment should reveal how someone approaches the work your team needs done. Tasks, submitted artifacts and explanations provide a stronger review basis than an isolated score.


Review submitted work, engineering decisions and criteria-based findings before the next technical conversation.
The solution or artifact produced during the exercise.
The candidate’s explanation of trade-offs and implementation choices.
Findings connected to the responsibilities being assessed.
Specific areas to explore further with a human technical reviewer.
The assessment policy should state whether AI tools are permitted and how their use will be evaluated before the exercise begins.
No. The task should match the role. Algorithmic skills may matter for some roles; production reasoning, debugging or architecture may matter more for others.
The submitted artifact, the task requirements, relevant reasoning and the evidence supporting the evaluation.
The assigned reviewers use the assessment evidence within the organization’s hiring process.