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Salary: USD 170,000 - 200,000 / annual
AssemblyAI builds foundational Voice AI models powering the next generation of voice applications. The company processes 1M+ hours of audio daily and serves 600M+ monthly inference calls, with thousands of customers including Granola, Fireflies, Figure AI, and CallRail. Operating as a capital-efficient, lean organization with under 100 people and ~$600K ARR per employee, AssemblyAI combines proven business scale with a small-team environment where individual impact is outsized.
You will own full-cycle recruiting for the Research team and a portfolio of senior Engineering roles, including Researchers, Research Engineers, and ML Engineers. This is a high-impact position where you'll work directly with research and engineering leadership to find, assess, and close some of the hardest-to-hire talent in tech.
Key responsibilities:
- Own end-to-end recruiting from intake through offer close for Research and senior Engineering roles
- Build deep, outbound-first pipelines by mapping talent at frontier labs, speech/audio teams, and academic groups; track publications, open-source contributors, and conference speakers
- Partner with research and engineering leaders to define candidate profiles, calibrate hiring bar, and design quality, efficient interview loops
- Conduct thoughtful technical screens and provide hiring managers with sharp, evidence-based candidate assessments
- Lead complex closes with competing offers from major labs, navigate equity conversations, and support international candidate onboarding in partnership with the People team
- Cultivate long-term relationships with candidates not yet ready to move
- Leverage data and AI tooling (smarter sourcing, Greenhouse pipeline management, funnel reporting) to work faster and more effectively
- Help scale the recruiting function through interviewer training, structured interview kits, and a candidate experience that reflects company values
Requirements:
- 5+ years of technical recruiting with a proven track record hiring ML researchers, research engineers, or ML/infrastructure engineers at AI labs, AI-native startups, or top tech companies
- Strong outbound sourcing practice and established network in the ML/AI research community
- Technical fluency sufficient to discuss model training, evaluation, and inference credibly with researchers; ability to distinguish strong profiles from keyword matches
- Experience closing senior, sought-after candidates against competing offers, including equity-heavy compensation packages
- Proficiency with Greenhouse, LinkedIn Recruiter, JuiceBox (or similar), and modern AI sourcing tools
- Self-directed approach: identify highest-impact work and execute without waiting to be asked
