Abnormal Security
501 - 1000 employees
🔒 Cybersecurity
Cybersecurity
Abnormal Security is a provider of cloud-native email security solutions that specializes in preventing a wide array of cyber threats. The company focuses on protecting businesses from attacks such as phishing, malware, ransomware, and social engineering, ensuring total protection for corporate email systems. Their offerings include business email security, phishing detection, and securing against account takeovers, making them a leader in the computer and network security space.
Machine Learning Engineer – I
Job not on LinkedIn
🔥 1 hour ago
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Abnormal Security
501 - 1000 employees
🔒 Cybersecurity
Cybersecurity
Abnormal Security is a provider of cloud-native email security solutions that specializes in preventing a wide array of cyber threats. The company focuses on protecting businesses from attacks such as phishing, malware, ransomware, and social engineering, ensuring total protection for corporate email systems. Their offerings include business email security, phishing detection, and securing against account takeovers, making them a leader in the computer and network security space.
📋 Description
• 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.
🎯 Requirements
• 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.
🏖️ Benefits
• Flexible work arrangements • Professional development opportunities
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