Machine Learning Engineer II

About the Role

Abnormal AI is seeking an experienced and technically strong Machine Learning Engineer (MLE) to join the Misdirected Email Prevention (MEP) team. The MEP 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 embeddings, 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 PM, TL, EM and UTL to align technical deliverables to roadmap milestones and ensure successful GA launches across supported environments
• Own the full ML lifecycle for MEP, including data wrangling, feature engineering, model training and evaluation, deployment, and monitoring. Delivering 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 shared team knowledge
• Participate in shared on-call rotation for owned components, with responsibilities focused on detection efficacy. 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
• 3+ years building and operating applied ML features in production systems
• Proven experience building end-to-end ML systems, including data wrangling (text and structured), feature engineering, model selection, training, evaluation, and production deployment with monitoring
• Demonstrate ability to implement and reason about algorithms, develop embeddings, average and combine signals, and apply numerical computing effectively
• Demonstrate ability to interrogate production data, identify behavioral or trend shifts, and launch targeted experiments to improve model efficacy
• Display 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, K8s, Django, Spark, Prometheus
• 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 leading a small team or project to deliver a feature or component from scratch

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