Machine Learning Engineer 1
Salary not listed • Remote • Remote - Singapore • Full-time
Posted 4d ago
About the job
Abnormal AI is seeking a Machine Learning Engineer - I to join the Misdirected Email Detection (MED) team, which plays a critical role in preventing accidental data loss by detecting and blocking misdirected outbound emails. This is a highly applied role for engineers who thrive on building, iterating, and experimenting, focusing on developing practical, end-to-end ML solutions rather than solely on model training. You will be responsible for generating and refining features, testing hypotheses, averaging signals, and translating research ideas into production-grade systems, 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.
Responsibilities
- Partner with Product Manager, Tech Lead, and engineering stakeholders to align technical deliverables to roadmap milestones and ensure successful GA launches.
- 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, focusing on detection efficacy and real-time scoring systems, including resolving efficacy-related alerts and investigating false positives/negatives.
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 for safe, scalable model deployments.
- Experience running offline metrics, online A/B tests, setting thresholds, and monitoring drift and performance, with guardrails and rollback strategies.
- Strong written and asynchronous communication skills.
- Effective working independently and across distributed, cross-functional teams.