Machine Learning Engineer – I

This is a fully remote position, open to applicants in Singapore.

📋 Description

• Collaborate with the Product Manager, Tech Lead, and engineering stakeholders to synchronize technical deliverables with roadmap milestones, ensuring successful GA launches across supported environments.

• Manage the entire ML lifecycle for Misdirected Email, which includes data wrangling, feature engineering, model training and evaluation, deployment, and monitoring. Deliver iterative enhancements with measurable reliability and customer impact.

• Conduct thorough experiments and evaluations (offline metrics, online A/B testing, post-launch monitoring), establish thresholds, and perform targeted error analysis to avoid regressions.

• Communicate effectively across different time zones, maintain high-quality technical documentation, and contribute to the collective knowledge of the team.

• Engage in a shared on-call rotation for managed components, with a focus on detection efficacy and real-time scoring systems. Key responsibilities include addressing efficacy-related alerts, investigating high-visibility false positives, and resolving reported false positives/false negatives from customers or internal teams.

⛳️ Requirements

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Information Systems, or a related engineering or quantitative field.

• Over 1 year of experience in building and operating applied ML features within production systems.

• Proven track record of contributing to end-to-end ML systems, including data wrangling (both text and structured), feature engineering, model selection, training, evaluation, and production deployment with monitoring.

• Demonstrated capability to implement and reason about algorithms, develop features, average and combine signals, and effectively apply numerical computing.

• Proven ability to analyze production data, identify behavioral or trend shifts, and initiate targeted experiments to enhance model efficacy.

• Understanding of online vs offline pipelines, data tables, and labeling workflows to effectively utilize tools that support safe and scalable model deployments.

• Experience in running offline metrics, conducting online A/B tests, establishing thresholds, and monitoring drift and performance, with guardrails and rollback strategies to ensure dependable iteration.

• Excellent written and asynchronous communication skills, with the ability to work independently and collaboratively across distributed, cross-functional teams.

🏝️ Benefits

• Flexible work arrangements

• Professional development opportunities