Netic

Netic

Founding Technical Recruiter

San Francisco

Sponsorship not specifiedDetected 418 days ago
DatabricksMachine LearningOutbound SalesRecruitingResearch

About the role

  • Netic is the AI revenue engine for essential services who are the backbone of the American economy.
  • There are now companies operating entirely AI-first on Netic.
  • This role combines strategic talent acquisition with hands-on recruiting, finding the exceptional engineers and operators who can solve the hardest problems in applied AI.

Responsibilities

  • Own full‑cycle hiring: Drive sourcing → close for engineers, ML researchers, and GTM technologists
  • Build outbound machinery: Design repeatable email, LinkedIn, and community hunts that surface 10× builders long before they're on the market.
  • Sell the mission: Deliver a crisp, credible pitch on Netic's frontier‑AI moat that converts "maybe" candidates into same‑day signers.
  • Own full‑cycle hiring: Drive sourcing → close for engineers, ML researchers, and GTM technologists; you're the single threaded owner on every job req.
  • Our Founding Technical Recruiter will build the team that builds the AI revenue engine.
  • Design repeatable email, LinkedIn, and community hunts that surface 10× builders long before they're on the market.

Requirements

  • Proven ability to spin up scalable interview frameworks and ATS automations without drowning candidates in steps.

Company info

  • refinement of first principles thinking, execution, and craftsmanship
  • We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
  • Obsess over customers in each line of code
  • With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America.

This listing is sourced directly from Netic's careers page and normalized into a canonical job model.