ML Engineer I — End-to-End Email Security ML

Abnormal; United States; Remote; USD 110,000 - 140,000; Full time

Job summary

Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. The role emphasizes practical, end-to-end ML systems, from data wrangling and feature engineering to deployment and monitoring in production environments.

You will partner with product and engineering stakeholders to align technical deliverables with roadmaps, run offline and online experiments, and deliver reliable detection capabilities with measurable customer impact in real-world

Qualifications

• 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.

Responsibilities

• 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.

Skills

Data wrangling

Feature engineering

Experimentation

Technical communication

Independent work

Education

BS degree in CS/ML/AI/Info Systems

Tools

Python

Go

AWS

Spark

Databricks

Job description

Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. The role emphasizes practical, end-to-end ML systems, from data wrangling and feature engineering to deployment and monitoring in production environments.

You will partner with product and engineering stakeholders to align technical deliverables with roadmaps, run offline and online experiments, and deliver reliable detection capabilities with measurable customer impact in real-world