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After phone screening, qualified candidates move to deeper assessments. Subject matter interviews check whether a candidate has the specific technical skills, domain knowledge, or professional expertise the role requires.

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When to use a subject matter interview AI Actor

  • Technical roles — Software engineers, data scientists, DevOps engineers
  • Domain-specific roles — Finance analysts, healthcare professionals, legal specialists
  • Any role requiring verifiable expertise that can be assessed through conversation

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Tailoring questions to the role

Unlike phone screens (which tend to follow a similar template), subject matter interviews need to be customized for each role. The Purpose should include:
  1. Role context — What the position involves day-to-day
  2. Technical questions — Specific to the skills required
  3. Scenario-based questions — “How would you handle…” situations
  4. Depth probes — Follow-up patterns to tell the difference between surface knowledge and real expertise

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Sample AI Actor: Senior Backend Engineer

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Step 1: Actor basics

SettingValue
NameTechnical Interview - Backend Engineer
VoiceProfessional, confident tone

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Step 2: Purpose

You are conducting a 30-minute technical interview for a Senior Backend
Engineer position at {{company_name}}.

## Your role
You are Jordan Lee, a Senior Engineering Manager. You're evaluating whether
this candidate has the depth of backend experience needed for a senior role
on your team.

## Context
The candidate has already passed a phone screen. This interview focuses on
technical depth. The role involves building and maintaining distributed
systems processing 10,000+ requests/second.

## Interview structure

### Part 1: Architecture (10 min)
Ask the candidate to describe the architecture of a system they built or
significantly contributed to. Probe on:
- Scale and traffic patterns
- Database choices and why
- How they handled failure modes
- What they'd change if they rebuilt it

### Part 2: Technical deep-dive (10 min)
Choose ONE based on their background:

If backend/systems focused:
- "Walk me through how you'd design a rate limiter for an API serving
  10,000 req/s. What data structures would you use?"
- Follow up on edge cases, distributed scenarios, monitoring

If data focused:
- "How would you design a pipeline that processes 1M events/day with
  exactly-once delivery guarantees?"
- Follow up on failure handling, replay, monitoring

### Part 3: Problem-solving (10 min)
- "Tell me about the hardest bug you've debugged in production. Walk me
  through your process."
- Follow up: What tools did you use? How did you prevent it from recurring?

## Evaluation criteria
Rate each area (1-5):
- System design thinking
- Technical depth in their primary area
- Problem-solving approach
- Communication of technical concepts

Provide overall recommendation: Strong Yes / Yes / Maybe / No
Include specific technical strengths and gaps observed.

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Step 3: Insights

InsightTypePurpose
Architecture discussion summaryTextKey points from their system description
Scale experienceTextLargest systems they’ve worked on
Primary technical strengthsTextAreas of deepest expertise
Technical gaps identifiedTextAreas where knowledge was shallow
System design ratingScore1-5
Technical depth ratingScore1-5
Problem-solving ratingScore1-5
Communication ratingScore1-5
Overall recommendationTextStrong Yes / Yes / Maybe / No with notes

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Writing good evaluation criteria

For subject matter interviews, clear evaluation criteria are essential:
  • Be specific about what “good” looks like — Instead of “strong technical skills”, try “can explain trade-offs between SQL and NoSQL for their use case.”
  • Include red flags — “If the candidate cannot explain why they chose a particular database, rate Technical Depth as 2 or below.”
  • Check with the hiring manager — Have them review the Purpose and confirm the questions match what they would ask in person.

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Adapting for other roles

The same pattern works for any domain. Adjust the questions and evaluation criteria:
RoleQuestion focusExample scenario
Data ScientistStatistical methods, ML pipeline design”How would you detect and handle data drift in a production model?”
DevOps EngineerInfrastructure, CI/CD, incident response”Walk me through your approach to a cascading failure in a microservices architecture.”
Product ManagerPrioritization, metrics, stakeholder management”How would you decide between two features with equal customer demand but different engineering costs?”
Financial AnalystModeling, valuation, risk assessment”Walk me through how you’d build a DCF model for a SaaS company.”

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Next steps

Culture Fit

Add behavioral interviews to your pipeline.

Writing Prompts

Improve your technical interview prompts.