System Design
Distributed Systems · Architecture Design
ScalabilityData modeling / storage
8 dimensions · 6 stages
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app.thebetterhire.ai/interviews/sarah-miller
Sarah Miller — Video interview · R2
14:32 / 32:08
85
Strong system design + clear communication. Some gaps on distributed systems trade-offs.
Strengths
Clean system design walkthrough
Articulate trade-off framing
Used real production examples
Concerns
Eventual consistency confusion
Light on observability practices
Hesitant on horizontal scaling
Next Steps
Probe distributed systems in R2
Pair on incident-response scenario
Schedule senior architect chat
Skill rounds
A round for each role, not one generic test
A working conversation with our AI interviewer, scored against a rubric we build with practitioners in that discipline.
Coding
Live Coding
Problem understandingAlgorithm & approach
8 dimensions · 4 stages
Product Sense
Product Judgment · Product Thinking
Problem framingPrioritization
8 dimensions · 6 stages
Product Analytics
Experimentation & A/B Testing · Metrics & Measurement
Metric definitionCausal reasoning
8 dimensions · 6 stages
Program Delivery
Technical Program Management · Project Management
Sequencing & prioritisationTrade-off ownership
8 dimensions · 6 stages
Site Reliability
DevOps · Production Operations
DetectionChange safety & rollback
8 dimensions · 6 stages
Platform Engineering
Developer Experience · Internal Developer Platform
Delivery mechanismSelf-service & standardisation
8 dimensions · 6 stages
Security Architecture
Cloud Security · Security Engineering
Threat modellingTrust boundaries & least privilege
8 dimensions · 6 stages
Where hiring meets intelligence
Match candidates to the role, run the rounds, and decide with evidence in front of you.
Match and score
Resumes matched to the role, then scored on every competency.
Resume Match
MC
Mike Chen
System DesignGoLeadership
92%Strong
RP
Raj Patel
Problem SolvingPythonDist. Systems
85%Good
SM
Sarah Miller
CommunicationJavaScale
71%Maybe
Competency Scorecard
MC
Mike ChenHIRE78Concern at 14:32 — weak on distributed systems
Skill Radar
MC
Mike Chen78 / 100 Competency Score
Run and record
Run by our AI or your team — timeline and audio behind every score.
Interview Status
MC
Mike Chen
System Design · 78/100 · 24 min
RP
Raj Patel
Coding · 14:32 elapsed
SM
Sarah Miller
Product Sense · Tomorrow 2:00 PM
Interview Timeline
2:10
Introduction & warm-up
8:45
Problem solving approachStrong
14:32
Distributed systemsConcern
22:05
Culture fit examplesStrong
Audio Evidence
Concern
“I think eventual consistency is when... the data just takes time to sync up?”
Showed uncertainty on distributed systems fundamentals. Follow-up recommended.
Rank and decide
Shortlist ranked on one rubric, debrief ready to send.
Candidate Ranking
MC
Mike Chen
System Design round · Low risk
RP
Raj Patel
Coding round · Med risk
SM
Sarah Miller
Product Sense round · Med risk
Head-to-Head
MC MikevsRaj RP
8.8Sys Design7.5
7.6Problem Slv8.2
7.8Comms6.8
8.0Culture Fit8.0
▲ Mike leads overall (78 vs 74)
Debrief Report
Backend Engineer Pipeline
Generated Apr 3, 2026
3Screened
2Recommended
1At Risk
✓ Sent to hiring-manager@company.com
Same platform
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GET /api/public/v1/interviews/{id}
Authorization: Bearer $API_KEY
{
"id": "iv_9f3c21",
"status": "completed",
"role": "Senior Backend Engineer",
"scorecard": {
"overall_score": 78,
"recommendation": "hire",
"summary": {
"strengths": ["Clean system design walkthrough"],
"concerns": ["Light on observability practices"],
"next_steps": ["Probe distributed systems in R2"]
},
"competencies": [ ... ]
},
"recording_url": "https://..."
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ADD-ON
Skill rounds
Coding, system design, product sense — deep, role-specific assessments with their own rubric.
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MCP (Model Context Protocol) is an open standard that lets AI assistants like ChatGPT and Claude use external tools. TheBetterHire is an MCP server — once connected, your AI can screen candidates, pull scorecards, and more without leaving the chat.
Any MCP-compatible client works. Today that includes ChatGPT, Claude (via Claude Desktop or Claude Code), OpenClaw, and many open-source clients. Setup takes one line of config.
When you trigger a screening, the candidate receives a link to an AI-conducted video interview. It takes up to 22 minutes, evaluates competencies relevant to the role, and delivers a scorecard with evidence to your AI assistant immediately after.
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