Machine Learning Engineer 1

Overview

Join the Misdirected Email Detection (MED) team to build, iterate on and productionize applied machine learning that prevents accidental data loss by detecting and blocking misdirected outbound emails.

What you'll do

• Partner with the 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, focused on detection efficacy and realtime scoring systems, including resolving efficacy-related alerts, investigating high-visibility false positives, and addressing reported false positives/false negatives from customers or internal teams.

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.
• 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 the 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 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.

Details

• Remote, Singapore.