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 tinkerers 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 shared team knowledge.
• Participate in shared 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 apply 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 from scratch.

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