🛠 Tech Stack

💼 About This Role

You'll lead the maturation of Securly's content classification system for K-12 student safety. You'll own the evaluation methodology, refactor models to multiclass, and set the standard for model cards across the team. This is applied ML with direct impact on student safety.

🎯 What You'll Do

• Define evaluation methodology and model card standards for content classification
• Lead multiclass refactor of content classification models
• Build and maintain labeled evaluation datasets with annotation workflows
• Connect offline evaluation to production monitoring for drift detection

📋 Requirements

• 5+ years in applied ML roles with multi-label/multi-class classification
• Production-quality Python with scikit-learn, PyTorch/TensorFlow, pandas, numpy
• Text/NLP feature engineering for URL tokenization and embedding-based representations
• ML evaluation rigor including precision/recall tradeoffs and confusion matrix analysis

✨ Nice to Have

• Experience with large-scale classification in production under latency constraints
• Knowledge of active learning / annotation workflows for imbalanced datasets
• Familiarity with web content/URL classification or K-12/CIPA compliance

🎁 Benefits & Perks

• 🏖️ Hybrid work model (2 days onsite)
• 📈 Mentorship opportunities for L3/L4 engineers
• 🔬 Applied ML impact on student safety at scale
• 🌐 Cross-team collaboration with engineering and product

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min