Machine Learning Engineer

Abnormal Security

. Join the Misdirected Email Detection team to prevent accidental data loss by detecting and blocking misdirected outbound emails .

Posted 10/27/2026full-timeRemote • SingaporeJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating end-to-end Machine Learning systems, including data wrangling, feature engineering, model training, and deployment. Strong communication skills and the ability to collaborate effectively across distributed teams are essential for success in this role.

Highest-signal resume keywords

End-To-End ML SystemsData WranglingFeature EngineeringModel TrainingPython

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAlgorithmsNumerical ComputingModel EvaluationThreshold SettingA/B TestingMonitoring DriftData InterrogationProduction DeploymentEmail Security

Soft Skills

Strong Written CommunicationIndependent WorkCross-Functional Collaboration

Tools & Technologies

AWSSparkDatabricksGo

Industry Keywords

Misdirected Email PreventionData Loss PreventionDetection EfficacyBehavioral AnalysisFalse Positives

Tech Stack

Tools & technologies

AWSPythonSparkGo

About the role

Key responsibilities & impact

Join the Misdirected Email Detection team to prevent accidental data loss by detecting and blocking misdirected outbound emails
Build, iterate, and experiment with practical, end-to-end ML solutions
Partner with Product Managers, Tech Leads, and engineering stakeholders to align technical deliverables with roadmap milestones and GA launches
Own the full ML lifecycle, including data wrangling, feature engineering, model training and evaluation, deployment, and monitoring
Run offline metrics, online A/B tests, post-launch monitoring, threshold setting, and targeted error analysis
Communicate across time zones, maintain technical documentation, and contribute to shared team knowledge
Participate in an on-call rotation for detection efficacy and real-time scoring systems
Resolve efficacy-related alerts, investigate false positives, and address false positives or false negatives reported by customers or internal teams

Requirements

What you’ll need

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, feature engineering, model selection, training, evaluation, production deployment, and monitoring
Ability to implement and reason about algorithms, develop features, average and combine signals, and apply numerical computing
Ability to interrogate production data, identify behavioral or trend shifts, and launch targeted experiments
Understanding of online and offline pipelines, data tables, and labeling workflows
Experience running offline metrics, online A/B tests, setting thresholds, monitoring drift and performance, and using guardrails and rollback strategies
Strong written and asynchronous communication skills
Effective ability to work independently and across distributed, cross-functional teams
Nice to have: Python, Go, AWS, Spark, and Databricks
Nice to have: experience in email security/DLP or misdirected email prevention
Nice to have: experience writing detectors/rules to complement ML models
Nice to have: experience operationalising research into reliable, customer-facing systems
Nice to have: prior experience contributing to a small team or delivering a feature or component from scratch

Benefits

Comp & perks

Pre-employment checks for conditional offers
Equal opportunity employment