Resources / Sample Report
This Is What You Get for Every Candidate
No black boxes. No vague recommendations. Every LayersRank assessment produces a detailed report with confidence-weighted scores, dimension breakdowns, and clear hiring signals. See exactly what our reports look like.
What’s in a LayersRank Report
Every assessment generates a comprehensive report
Executive Summary
- Overall score with confidence interval
- Hiring recommendation (Strong Hire / Hire / Maybe / No Hire)
- Risk assessment (Low / Medium / High)
- Key strengths and concerns in 2–3 sentences
Dimension Scores
- Technical: System design, debugging, depth, trade-offs
- Behavioral: Communication, collaboration, feedback response
- Contextual: Role understanding, motivation, trajectory
- Each with score (0–100), confidence %, and interval (±)
Question-by-Question Breakdown
- Candidate’s actual response (text or transcript)
- Individual question score
- Model agreement level
- Specific feedback on response quality
Behavioral Signals
- Response timing patterns
- Authenticity indicators
- Engagement metrics
- Any flags or concerns
Comparison Context
- How this candidate compares to others for this role
- Percentile ranking where available
Actionable Recommendations
- Clear next steps for hiring team
- Specific areas to probe in final rounds
- Reference check focus areas
Sample Report
Priya Sharma — Senior Backend Engineer
Assessment Date: January 15, 2025 · Report Generated: January 16, 2025
Candidate Report
Priya Sharma
Senior Backend Engineer · Requisition #7294
Assessment completed January 15, 2025
78 ± 4
Overall Score /100
87%
Overall Confidence
Risk: Low
Summary
Strong technical candidate with demonstrated system design capability and clear communication. Solid behavioral examples showing collaboration experience. Minor concern around stakeholder management at scale — probe in final round. Recommend advancing to technical deep-dive.
Dimension Scores
confidence 91%
confidence 84%
confidence 88%
Key Strengths
- Exceptional system design thinking with unprompted consideration of failure modes
- Clear, structured communication style
- Strong examples of cross-functional collaboration
Areas to Probe
- Limited examples of managing up to senior leadership
- Mentioned "sometimes frustrated" with ambiguous requirements — explore further
Generated by LayersRank · Report ID: LR-2025-SBE-004721
Confidential · For authorized reviewers only
Technical Dimension (82 ± 3)
Question-by-question breakdown
Q1: System Design
“Walk through how you’d design a notification service handling 10 million daily users. Consider delivery guarantees, scale, and failure scenarios.”
Response Summary
Candidate proposed tiered architecture with separate ingestion, processing, and delivery layers. Discussed WebSocket for real-time vs. batch for email. Addressed failure modes with dead-letter queues and retry logic. Provided quantified throughput estimates.
Evaluation
Feedback
Excellent response demonstrating practical system design experience. Candidate considered scale, reliability, and failure modes without prompting. Could have gone deeper on consistency guarantees.
Q2: Debugging Methodology
“You get a 3am page for increased API latency. Walk through your first 30 minutes of investigation.”
Response Summary
Candidate described systematic approach: check monitoring dashboards, identify affected endpoints, review recent deployments, check database connections, examine error rates. Mentioned specific tools (Datadog, PagerDuty).
Evaluation
Feedback
Good operational maturity. Systematic approach with clear prioritization. Would benefit from more detail on stakeholder communication during incidents.
Q3: Technical Trade-offs
“When would you choose eventual consistency over strong consistency? Give a specific example.”
Response Summary
Candidate explained CAP theorem trade-offs, gave example of social media feed where eventual consistency is acceptable vs. payment processing where strong consistency is required. Discussed user experience implications.
Evaluation
Feedback
Solid understanding of consistency trade-offs with practical examples. Response was comprehensive but slightly textbook — probe for more nuanced real-world scenarios in final round.
Q4: Code Review
“How do you approach giving feedback on a junior engineer’s pull request that has significant issues?”
Response Summary
Candidate emphasized starting with positives, being specific about issues, explaining the “why” behind suggestions, offering to pair on fixes. Mentioned checking if issues are patterns vs. one-offs.
Evaluation
Feedback
Excellent approach to code review feedback. Demonstrates mentorship capability and collaborative mindset.
Behavioral Dimension (75 ± 5)
Collaboration, feedback, and communication
Q5: Collaboration Example
“Tell me about a time you worked with a product manager who had different priorities than your engineering team.”
Response Summary
Candidate described situation where PM wanted quick feature release but engineering had concerns about technical debt. Explained how they quantified the debt cost, proposed phased approach, and found compromise.
Evaluation
Feedback
Good example of cross-functional navigation. Would benefit from more detail on how the compromise was reached and what candidate specifically did vs. the team.
Q6: Receiving Feedback
“Tell me about a time you received critical feedback on your technical approach. How did you respond?”
Response Summary
Candidate described situation where senior engineer disagreed with their database choice. Initially defensive but “took a day to think about it” and realized the feedback was valid. Changed approach.
Evaluation
Feedback
Honest response showing self-awareness. The initial defensiveness is common but worth probing — does this still happen? What triggers it?
Adaptive Follow-Up Triggered
“You mentioned initially feeling defensive. What helps you move past that reaction now?”
“I’ve learned to ask for time to process before responding. If I feel defensive, that’s usually a signal the feedback has merit and I need to sit with it. I also try to separate the feedback from the person giving it.”
Strong self-awareness and developed coping mechanism. Confidence increased from 74% to 81%.
Q7: Communication Under Pressure
“Describe a situation where you had to communicate a technical problem to non-technical stakeholders.”
Response Summary
Candidate described explaining a security vulnerability to executive team. Used analogy of “leaving the back door unlocked” rather than technical jargon. Focused on business impact and remediation timeline.
Evaluation
Feedback
Good communication instincts. Effective use of analogy and focus on business impact rather than technical details.
Contextual Dimension (77 ± 4)
Role understanding and motivation
Q8: Role Understanding
“What do you think will be the biggest challenges in this Senior Backend Engineer role?”
Response Summary
Candidate identified scale challenges (moving from thousands to millions of users), team dynamics (joining established team with existing patterns), and technical debt (balancing new features vs. maintenance).
Evaluation
Feedback
Good understanding of role challenges. Shows candidate has thought about what they’re signing up for.
Q9: Motivation
“What specifically attracted you to this role versus other opportunities you’re considering?”
Response Summary
Candidate mentioned interest in the domain (fintech), team reputation, and opportunity to work at scale. Also mentioned compensation and remote flexibility.
Evaluation
Feedback
Reasonable motivation with a mix of intrinsic and extrinsic factors. Not the most passionate response, but no red flags.
Behavioral Signals
Authenticity and engagement metrics
Response Authenticity
Engagement Metrics
Comparison Context
This candidate vs. role benchmark (n=47 candidates)
Overall Score
78
71st percentile
Technical
82
78th percentile
Behavioral
75
62nd percentile
Contextual
77
65th percentile
Recommended Next Steps
What to do with this candidate
Advance to final round
Technical deep-dive with senior engineer
Probe areas
- • Stakeholder management at scale (limited examples)
- • Defensive reaction to feedback (has coping mechanism, but verify)
Reference check focus
Collaboration patterns, how they handle ambiguity
Timeline
Candidate mentioned 2-week notice period; can start quickly
Report Metadata
What Makes This Report Different
More than a score
Confidence Levels
Traditional reports say “Score: 78.” LayersRank says “Score: 78 ± 4, 87% confidence.” You know when to trust the signal and when to probe further.
Adaptive Follow-Up
Notice Q6 triggered a follow-up question when initial confidence was low. The system detected ambiguity and probed it, improving assessment quality.
Specific Feedback
Not just scores — specific feedback on each response that you can use in final rounds or share with candidates.
Behavioral Authenticity
We track signals that indicate genuine responses vs. concerning patterns. This candidate showed high authenticity.
Actionable Recommendations
Clear next steps: what to probe, what to verify, how to proceed. Every report gives your hiring team a playbook.
Download Options
Get the full sample report
Full Sample Report (PDF)
Complete 12-page report with all sections, formatting, and branding.
Download PDFReport Template Documentation
Technical documentation of all report fields and how they’re calculated.
View DocumentationRelated Resources
See Your Own Reports
Want to see LayersRank reports for your actual candidates? Start a free trial and assess your first 5 candidates at no cost.