Posted May 7, 2026
As the Lead of Securly's content classification system, you will be responsible for maturing the ML infrastructure that determines the appropriateness of web content for K-12 students. Your role will involve building and refining classification models, establishing evaluation frameworks, and ensuring the accuracy and reliability of the content safety system. You will be at Level 5 (L5), serving as the technical leader for content safety within the data science function. Key Responsibilities:
Define the evaluation methodology for content classification at Securly and set the standard for model cards before model releases. - Lead the transition of binary models to multiclass classification for various content categories such as Adult Content, Violence, Self-Harm, Social Media, etc. - Develop and manage labeled evaluation datasets with robust annotation workflows, addressing class imbalance and label noise systematically. - Connect offline evaluation processes to production monitoring to prevent customer-facing issues related to classification accuracy. - Investigate and resolve misclassification errors, providing detailed root cause analyses for false positives and false negatives. - Mentor the AI team on evaluation methodology, model development practices, and effective communication of data science findings. - Collaborate with engineering to integrate model outputs into the production filtering stack with the necessary latency and reliability constraints. - Research and prototype improvements in feature representations, model architectures, and label efficiency for emerging content categories. Qualifications Required:
5+ years of experience in applied ML roles with expertise in multi-label/multi-class classification, model evaluation methodology, and feature engineering for text and URL data using Python (ML stack). - Proficiency in text/NLP feature engineering, ML evaluation rigor, data engineering for ML, and technical communication to present findings to both technical and non-technical stakeholders. - Experience in large-scale classification in production, active learning/annotation workflows, and cloud ML infrastructure like AWS SageMaker or GCP Vertex AI is strongly preferred. - Knowledge of web content/URL classification domain, K-12/CIPA compliance, LLM-based classification, and graph/network features is a plus. Benefits:
Securly offers a comprehensive benefits package including health insurance, life insurance, learning & development reimbursement, paid time off, retirement benefits, parental leave, and public holidays. As the Lead of Securly's content classification system, you will be responsible for maturing the ML infrastructure that determines the appropriateness of web content for K-12 students. Your role will involve building and refining classification models, establishing evaluation frameworks, and ensuring the accuracy and reliability of the content safety system. You will be at Level 5 (L5), serving as the technical leader for content safety within the data science function. Key Responsibilities:
Define the evaluation methodology for content classification at Securly and set the standard for model cards before model releases. - Lead the transition of binary models to multiclass classification for various content categories such as Adult Content, Violence, Self-Harm, Social Media, etc. - Develop and manage labeled evaluation datasets with robust annotation workflows, addressing class imbalance and label noise systematically. - Connect offline evaluation processes to production monitoring to prevent customer-facing issues related to classification accuracy. - Investigate and resolve misclassification errors, providing detailed root cause analyses for false positives and false negatives. - Mentor the AI team on evaluation methodology, model development practices, and effective communication of data science findings. - Collaborate with engineering to integrate model outputs into the production filtering stack with the necessary latency and reliability constraints. - Research and prototype improvements in feature representations, model architectures, and label efficiency for emerging content categories. Qualifications Required:
5+ years of experience in applied ML roles with expertise in multi-label/multi-class classification, model evaluation methodology, and feature engineering for text and URL data using Python (ML stack). - Proficiency in text/NLP feature engineering, ML evaluation rigor, data
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