Artificial Intelligence · Executive search
AI hiring, done right.
Where the talent market is overheated, the buyers don't yet know what they want, and the technology shifts quarterly.
Grant's record in AI
- Placements
- 512
- Avg savings/hire
- ~$110K
- Typical base
- $300–500K
- Y1 retention
- 92%
The view from 30 years
How AI hiring is genuinely different.
AI hiring is the most volatile market Grant has worked in 30 years. Comp expectations are detached from gravity, candidate signals are gameable, and the technology a leader was hired for in Q1 may be deprecated by Q3. Grant has placed leaders into research labs, infrastructure platforms, and applied-AI startups — including four candidates who later joined frontier labs.
Three signals
What we screen for that generalists miss.
Comp benchmarking is a moving target
An ML research lead at $400K base in 2024 is at $700K in 2026. We re-benchmark every search, every quarter — using actual offers extended, not LinkedIn salaries.
Research vs product vs infra are three different hires
A research scientist will not become a successful VP Product. A platform engineer will not become a successful research lead. We screen for the right archetype on the right role — not pattern-match on 'AI'.
Hype-cycle resistance is a soft skill we test for
Candidates who've worked at four hot AI startups in three years are an attrition risk. We probe for what they've actually shipped, not what they've worked on.
The pattern we see most often
Paying frontier-lab comp for an applied-AI shipping role
Founders read about $5M packages at OpenAI and assume their applied-AI VP needs a similar package. They don't. The right hire for an applied-AI startup is often a senior platform engineer with strong ML ops instincts, at a third the cost of a research lead. We help calibrate the role before we calibrate the offer.
Ask: 'What does your current production model do, and how do you know it works?' If they pivot to talking about the model architecture instead of the eval framework, they're a research candidate, not a product candidate. Useful to know — for the right role.
The signal we look for
Recent placements
Anonymized — but real.
Every placement in the last 18 months. Client names and personal details withheld; structure and timeline are exact.
Where we source
The companies we mine for AI leaders.
Primary feeders supply roughly 60% of our shortlists. Adjacent industries fill the rest — Grant's pattern recognition tells us which non-obvious feeders punch above their weight for this vertical.
Primary feeders
Adjacent industries
- Cloud Infrastructure
- Data Science
- Enterprise Software
AI executive search
Your next AI hire, without the recruiter premium.
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