Machine Learning Engineer I - Message Security Products

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 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.

#LI-UC1

• Highlight any production‑grade ML pipelines you have built, especially those involving text data or real‑time scoring.
• Prepare a short case study that walks through your end‑to‑end process: data wrangling, feature engineering, model selection, deployment, and monitoring.
• Demonstrate familiarity with Python and cloud services (AWS, Spark) in your technical interview; a quick coding exercise is likely.
• Emphasize strong written communication by sharing clear documentation or blog posts you have authored about past projects.
• During on‑call scenario questions, focus on how you triage false positives/negatives and implement guardrails.
• If you have any experience in email security or DLP, bring it up early to differentiate yourself.
• Be prepared to discuss how you measure model drift and what rollback strategies you have used in production.