Verisoul

Verisoul

Intelligence Engineer - Offline

Austin

Sponsorship not specified$70k-$105kDetected 219 days ago
Full-Stack DevelopmentAlgorithmsData Engineering

About the role

  • About Verisoul Verisoul stops fake accounts and fraud in the era of AI.
  • We distinguish real humans from bots and bad actors for high-growth companies like Clay, Augment Code, and Morning Consult.
  • Analyze history, reputation, and patterns to catch sophisticated fraud that real-time checks miss.

Responsibilities

  • Architect Reputation Systems: Build batch and nearline pipelines to score risk across identity attributes (email, phone, ISP) and lay the foundation for a scalable intelligence knowledge graph.
  • Develop Probabilistic Models: Engineer scoring algorithms that integrate historical reputation data and quantify uncertainty for dynamic entities like residential IPs.
  • Build the deep memory of the system.
  • Build batch and nearline pipelines to score risk across identity attributes (email, phone, ISP) and lay the foundation for a scalable intelligence knowledge graph.
  • All-in Optimism: Building a startup is hard.
  • Own high-impact problems end-to-end, combining structure with the execution skills to get the job done.

Requirements

  • Experience with batch processing, data pipelines, and warehousing.
  • Big Data Engineering: Experience with batch processing, data pipelines, and warehousing.

Skills

  • Be a self-starter and a self-completer.

Compensation

  • $200,000 - $250,000 + Equity Grant
  • Base Salary: $190,000 - $240,000 fixed annual salary (All Base)
  • 401k match: 4% match of compensation (~$8,000)
  • Life At Verisoul
  • In office 5 days a week in Austin, Texas!
  • Free Lunch - personalized your way

Benefits

  • $10K Relocation Bonus
  • 100% Insurance Coverage
  • Unlimited Vacation

Company info

  • 6x ARR growth (to $2M) and scaled to 100+ customers.
  • First Principles Curiosity: Challenge assumptions with a beginner's mind.

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