Machine Learning Engineer I - Message Security Products

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

Job summary

Abnormal AI is seeking a Machine Learning Engineer I for the Misdirected Email Detection (MED) team to build practical, production-grade ML solutions that prevent misdirected outbound emails at scale.

You will own the full ML lifecycle—from data wrangling and feature engineering to training, evaluation, deployment, and monitoring—while collaborating with product, tech leads, and engineers to deliver measurable customer impact in real-world environments.

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.
• 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.

Responsibilities

• 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.

Skills

Data wrangling

Feature engineering

Model training

Model evaluation

Production deployment

Experimental design

Numerical computing

End-to-end ML systems

Online vs offline pipelines

Education

BS degree in Computer Science, ML, AI, Information Systems, or related field

Job description

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 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 labeli