interview-system-designer

Interview System Designer

The agent designs role-specific interview loops, generates competency-based question banks with scoring rubrics, and detects interviewer bias through statistical calibration analysis.

Quick Start

# Design a complete interview loop for a senior software engineer role
python loop_designer.py --role "Senior Software Engineer" --level senior --team platform --output loops/

# Generate a question bank for a product manager position
python question_bank_generator.py --role "Product Manager" --level senior --competencies leadership,strategy,analytics --output questions/

# Analyze interview calibration across candidates and interviewers
python hiring_calibrator.py --input interview_data.json --output calibration_report.json --analysis-type full

Core Workflows

Workflow 1: Design an Interview Loop

1. Define role requirements (title, level, team, 3-5 critical competencies)
2. Run loop_designer.py with role parameters to generate rounds, time allocations, and scorecards
3. Review generated loop for competency coverage -- every required competency maps to at least one round
4. Customize interviewer skill requirements per round
5. Validation checkpoint: 100% competency coverage; no round exceeds 90 minutes; total loop under 6 hours

python loop_designer.py --role "Staff Data Scientist" --level staff \
  --competencies ml,statistics,leadership --format json --output loops/ds-staff.json

Workflow 2: Generate a Question Bank

1. Identify target role and experience level
2. Select competency areas and question types (technical, behavioral, situational)
3. Run question_bank_generator.py to produce questions with scoring rubrics
4. Review for duplicate or overlapping questions across competency areas
5. Validation checkpoint: <15% duplicate rate; each competency has 3+ questions; calibration examples (poor/good/great) present for every question

python question_bank_generator.py --role "Frontend Engineer" \
  --competencies react,typescript,system-design --num-questions 30

Workflow 3: Calibrate Hiring Bar

1. Collect interview results data (minimum 10 records for statistical significance)
2. Run hiring_calibrator.py with comprehensive analysis
3. Review interviewer deviation metrics -- flag anyone >0.5 standard deviations from team mean
4. Generate coaching recommendations for flagged interviewers
5. Validation checkpoint: Bias detection precision >80%; score distribution follows target (20/40/30/10 split)

python hiring_calibrator.py --input q1_interviews.json \
  --analysis-type comprehensive --trend-analysis --period quarterly

Interview Loop Templates

Software Engineering Loops

| Level | Duration | Rounds | Focus Areas | |-------|----------|--------|-------------| | Junior/Mid (2-4 yr) | 3-4 hours | 3-4 | Coding fundamentals, debugging, system basics, growth mindset | | Senior (5-8 yr) | 4-5 hours | 4-5 | System design, technical leadership, mentoring, code quality | | Staff+ (8+ yr) | 5-6 hours | 5-6 | Architecture vision, org impact, technical strategy, cross-functional leadership |

Senior Software Engineer Example:

1. Technical Phone Screen (45min) -- Advanced algorithms, optimization
2. System Design (60min) -- Scalability, trade-offs, architectural decisions
3. Coding Excellence (60min) -- Code quality, testing strategies, refactoring
4. Technical Leadership (45min) -- Mentoring, technical decisions, cross-team collaboration
5. Behavioral & Culture (30min) -- Leadership examples, conflict resolution

Sample Questions by Level

Junior: "Implement a function to find the second largest element in an array" Senior: "Design a real-time chat system supporting 1M concurrent users" Staff+: "How would you evaluate and introduce a new programming language to the organization?"

Behavioral (STAR Method):

• "Tell me about a time you had to influence a decision without formal authority"
• "Walk me through a time when you had to make a decision with incomplete information"

Scoring Rubric

4-Point Scale

| Score | Label | Description | |-------|-------|-------------| | 4 | Exceeds | Demonstrates mastery beyond required level | | 3 | Meets | Solid performance meeting all requirements | | 2 | Partial | Shows potential but has development areas | | 1 | Does Not Meet | Significant gaps in required competencies |

Calibration Benchmarks

• Target distribution: 20% (4s), 40% (3s), 30% (2s), 10% (1s)
• Interviewer consistency: <0.5 std dev from team average
• Pass rate: 15-25% for most roles
• New hire correlation: >0.6 between interview scores and 6-month performance

Anti-Patterns

• Unstandardized loops -- different question sets per candidate prevent fair comparison; always use structured guides
• Halo effect scoring -- one strong answer inflates all dimensions; score each competency independently before debrief
• Similarity bias -- favoring candidates with similar backgrounds; require diverse panels and rotate assignments
• Skipping calibration -- interviewers drift over time without regular calibration sessions (monthly minimum)
• Over-indexing on algorithms -- testing LeetCode for a staff role that requires architecture and leadership; match round focus to actual job requirements
• No debrief structure -- unstructured debriefs lead to anchoring on the loudest voice; require independent score submission before group discussion

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | Loop designer produces generic rounds with no role-specific focus | The --competencies flag was omitted, so the tool falls back to default competency mapping for the role family | Re-run with explicit --competencies listing the 3-5 most critical skills for the position | | Question bank output has too many behavioral questions and too few technical ones | The --question-types flag was not provided, causing the generator to use a balanced default split | Supply --question-types technical,system-design (or whichever mix is needed) to control the ratio | | Hiring calibrator reports "insufficient data" for bias detection | The input JSON contains fewer than 10 interview records, which is below the statistical minimum | Collect more interview data before running bias analysis; use --analysis-type scoring for small datasets | | Calibrator trend analysis returns empty results | The input data lacks date fields or all records fall within a single period | Ensure each interview record has a valid date field and that the dataset spans multiple periods matching --period | | Loop designer ignores the --team flag | The team value does not match any of the predefined team mappings in the tool | Check supported team names in the tool's TEAM_CONFIGS dictionary, or omit --team and rely on competency overrides | | Score distribution chart shows all interviewers clustered at the same score | Interviewers are not applying the full 1-4 rubric scale (central tendency bias) | Run --analysis-type calibration to identify leniency/severity patterns and use the coaching recommendations | | Question bank generates duplicate questions across competency areas | Overlapping competency keywords (e.g., "leadership" appears in both behavioral and technical mappings) | Use more specific competency terms or reduce --num-questions to avoid exhausting the unique question pool |

Success Criteria

• Interview loop coverage: Every generated loop maps 100% of required competencies to at least one round with a dedicated scoring dimension.
• Question bank diversity: Generated banks contain no more than 15% duplicate or near-duplicate questions across competency areas.
• Calibration detection accuracy: Bias detection flags interviewer score deviation greater than 0.5 standard deviations from the team mean with at least 80% precision.
• **Tim