Machine Learning Engineer I

About the Role Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. The MED team plays a critical role in preventing accidental data loss by detecting and blocking misdirected outbound emails, delivering protection at scale without adding operational burden to customer SOCs. This is a highly applied role for MLEs who thrive on building, iterating, and experimenting. Rather than focusing solely on model training, you will also be responsible for developing practical, end-to-end ML solutions. This includes but is not limited to generating and refining features, testing hypotheses, averaging signals, and translating research ideas into production-grade systems, all while collaborating cross-functionally to turn customer needs into measurable product improvements. The ideal candidate combines a tinkerer's mindset with technical rigor, balancing innovation with production excellence to drive experimentation, scale solutions, and deliver reliable detection capabilities that create meaningful customer impact in real-world environments. What you will do - Partner with Product Manager, Tech Lead and engineering stakeholders to align technical deliverables to roadmap milestones and ensure successful GA launches across supported environments. - Own the full ML lifecycle for Misdirected Email, including data wrangling, feature engineering, model training and evaluation, deployment, and monitoring. Deliver iterative improvements with measurable reliability and customer impact. - Run rigorous experiments and evaluations (offline metrics, online A/B testing, post-launch monitoring), set thresholds, and conduct targeted error analysis to prevent regressions. - Communicate effectively across time zones, maintain high-quality technical documentation, and contribute to d team knowledge. - Participate in d on-call rotation for owned components, with responsibilities focused on detection efficacy and realtime scoring systems. Priorities include resolving efficacy-related alerts, investigating high-visibility false positives, and addressing reported false positives/false negatives from customers or internal teams. Must Haves - BS degree in Computer Science, Machine Learning, Artificial Intelligence, Information Systems, or a related engineering or quantitative field. - 1+ years building and operating applied ML features in production systems. - Proven experience contributing to end-to-end ML systems, including data wrangling (text and structured), feature engineering, model selection, training, evaluation, and production deployment with monitoring. - Demonstrated ability to implement and reason about algorithms, develop features, average and combine signals, and numerical computing effectively. - Demonstrated ability to interrogate production data, identify behavioral or trend shifts, and launch targeted experiments to improve model efficacy. - Understanding of online vs offline pipelines, data tables and labeling workflows to effectively leverage tooling to support safe, scalable model deployments. - Experience running offline metrics, online A/B tests, setting thresholds, and monitoring drift and performance, with guardrails and rollback strategies to ensure reliable iteration. - Strong written and asynchronous communication skills. Effective working independently and across distributed, cross-functional teams. Nice to Have - Experience with our stack: Python, Go, AWS, Spark, Databricks - Experience in email security/DLP or misdirected email prevention domains and customer-focused ML deployments. - Experience writing detectors/rules to complement ML models for safe launches and rapid iteration. - Experience with operationalising research into reliable, customer-facing systems, with emphasis on scalability, performance, and detection accuracy in real-world environments. - Prior experience contributing to a small team or project to deliver a feature or component