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Masarna runs AI, ML, and data searches for startups that need technical judgment — not keyword matching — on roles where the market is noisy and the cost of a miss is high.
“AI Engineer” can mean research, applied ML, platform, or prompt-heavy product work. Without calibration, you interview the wrong people.
Hiring managers are spending cycles on candidates who can't clear the architecture, evaluation, or production bar.
You're guessing on ranges, equity, and which companies are actually transferable talent pools for your stack.
You're racing model, product, or customer deadlines — and still need screened shortlists, not volume.
We map adjacent titles and real skill graphs — not just the label on the req.
Builders who ship models into products: evaluation, fine-tuning, retrieval, and production reliability.
AI Engineer · ML Engineer · Applied Scientist · LLM Engineer
People who make training and inference data trustworthy — pipelines, platforms, and analytics foundations.
Data Engineer · Analytics Engineer · ML Platform · Data Scientist
Leaders who can hire, set quality bars, and connect research ambition to shipping reality.
Head of AI · ML Manager · Staff+/Principal technical leads
We separate research vs. applied, training vs. inference, and product surface area — then lock a scorecard hiring managers can use.
Target companies, adjacent titles, and ecosystems that produce the skills you need — not just people with “AI” in their headline.
Technical screens cover depth, shipping history, evaluation craft, and motivation — with notes your hiring managers can trust.
Only scorecard-fit candidates reach your team. We coordinate loops, chase feedback, and keep the pipeline honest every week.
25 minutes · Speak directly with a senior recruiter. Bring the role, the bar, and the constraints — we'll tell you how we'd run the search.
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