Guides & Resources
Practical resources for companies and recruiting firms evaluating AI, ML, and data science candidates.
The AI/ML Interview Evaluation Guide
A practical guide to evaluating AI and ML candidates — what to look for, what to ignore, and how to distinguish signal from fluency.
- Competency frameworks
- Question design
- Scorecard structure
- Common failure modes
Hiring AI Engineers vs. ML Engineers: A Decision Framework
When to hire an AI engineer versus an ML engineer — how the roles differ, when you need one versus the other, and what to look for in each.
- Role differentiation
- Skill mapping
- Seniority levels
- Interview calibration
Building a Recruiting Firm Technical Evaluation Practice
For staffing firms placing AI/ML talent: how to add structured technical evaluation to your process without building an internal interview team.
- White-label evaluation
- Client credibility
- Candidate qualification
- Process integration
Guides are available to clients and prospective clients. Book a call or purchase an interview to get access.
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