Machine Learning Engineer I (AI)

TL;DR

Machine Learning Engineer I (AI/Cybersecurity): Building and operating end-to-end ML solutions for misdirected email detection to prevent accidental data loss with an accent on practical, production-grade ML systems. Focus on feature engineering, translating research ideas into scalable production systems, and conducting rigorous A/B testing to improve detection efficacy.

Location: Must be eligible to access controlled technology under U.S. export control laws (EAR)

Company

hirify.global provides AI-powered email security solutions designed to prevent data loss and detect sophisticated cyber attacks.

What you will do

• Own the full ML lifecycle for Misdirected Email detection, including data wrangling, feature engineering, model training, evaluation, and deployment.
• Partner with Product Managers and Tech Leads to align technical deliverables with roadmap milestones and ensure successful launches.
• Execute rigorous experiments using offline metrics, online A/B testing, and post-launch monitoring to prevent regressions.
• Maintain high-quality technical documentation and collaborate effectively across distributed, cross-functional teams.
• Participate in an on-call rotation focused on detection efficacy and the reliability of real-time scoring systems.

Requirements

• BS degree in Computer Science, Machine Learning, AI, Information Systems, or a related quantitative field.
• 1+ years of experience building and operating applied ML features in production environments.
• Proven experience with end-to-end ML systems, including data wrangling of text and structured data.
• Ability to implement algorithms, develop features, and apply numerical computing effectively.
• Experience running offline metrics, online A/B tests, and monitoring model drift and performance.
• Must be eligible to access controlled technology under U.S. export control laws (EAR).

Nice to have

• Proficiency with Python, Go, AWS, Spark, and Databricks.
• Experience in email security, Data Loss Prevention (DLP), or misdirected email prevention.
• Ability to write detectors or rules to complement ML models for safer and faster iteration.
• Experience operationalizing research into scalable, customer-facing systems.

Culture & Benefits

• Competitive compensation including base salary, equity, and annual bonus.
• Comprehensive employee benefits package.
• Inclusive work environment committed to equal opportunity employment.
• Opportunity to work with a distributed team using AI-assisted recruiting tools.

Hiring process

• Resume analysis using AI-assisted tools to identify key areas for exploration.
• Video interviews with identity validation at various stages.
• Pre-employment background checks for successful candidates.