Subject Matter Interviews

Subject matter interviews check whether a candidate has the specific technical skills, domain knowledge, or professional expertise the role requires.

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

Tailoring questions to the role

Subject matter interviews need to be customized for each role. The Purpose should include:

Role context — What the position involves day-to-day
Technical questions — Specific to the skills required
Scenario-based questions — “How would you handle…” situations
Depth probes — Follow-up patterns to tell the difference between surface knowledge and real expertise

Sample AI Actor: Senior Backend Engineer

Step 1: Actor basics

Setting	Value
Name	Technical Interview - Backend Engineer
Voice	Professional, confident tone

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
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.

Step 3: Insights

Insight	Type	Purpose
Architecture discussion summary	Text	Key points from their system description
Scale experience	Text	Largest systems they’ve worked on
Primary technical strengths	Text	Areas of deepest expertise
Technical gaps identified	Text	Areas where knowledge was shallow
System design rating	Score	1-5
Technical depth rating	Score	1-5
Problem-solving rating	Score	1-5
Communication rating	Score	1-5
Overall recommendation	Text	Strong Yes / Yes / Maybe / No with notes

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.

Adapting for other roles

The same pattern works for any domain. Adjust the questions and evaluation criteria:

Role	Question focus	Example scenario
Data Scientist	Statistical methods, ML pipeline design	”How would you detect and handle data drift in a production model?”
DevOps Engineer	Infrastructure, CI/CD, incident response	”Walk me through your approach to a cascading failure in a microservices architecture.”
Product Manager	Prioritization, metrics, stakeholder management	”How would you decide between two features with equal customer demand but different engineering costs?”
Financial Analyst	Modeling, valuation, risk assessment	”Walk me through how you’d build a DCF model for a SaaS company.”

Introduction

Getting Started

Setup Guides

How-to Guides

Use Cases

Subject Matter Interviews

When to use a subject matter interview AI Actor

Tailoring questions to the role

Sample AI Actor: Senior Backend Engineer

Step 1: Actor basics

Step 2: Purpose

Step 3: Insights

Writing good evaluation criteria

Adapting for other roles

Next steps

Culture Fit

Actor Purpose

​When to use a subject matter interview AI Actor

​Tailoring questions to the role

​Sample AI Actor: Senior Backend Engineer

​Step 1: Actor basics

​Step 2: Purpose

​Step 3: Insights

​Writing good evaluation criteria

​Adapting for other roles

​Next steps

Culture Fit

Actor Purpose

When to use a subject matter interview AI Actor

Tailoring questions to the role

Sample AI Actor: Senior Backend Engineer

Step 1: Actor basics

Step 2: Purpose

Step 3: Insights

Writing good evaluation criteria

Adapting for other roles

Next steps