ML Engineer I: End-to-End Misdirected Email Detection

Abnormal AI; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. This highly applied role emphasizes building, iterating, and delivering practical ML solutions from data wrangling to production deployment.

You will translate research ideas into robust systems that improve detection accuracy and scale with customer impact. The role requires strong Python/Go experience, production ML background, and ability to collaborate across distributed teams.

Qualifications

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

Responsibilities

• Own the full ML lifecycle for Misdirected Email, including data wrangling, feature engineering, model training, evaluation, deployment, and monitoring.
• Run rigorous experiments and evaluations (offline metrics, online A/B testing, post-launch monitoring) and set thresholds.
• Communicate effectively across time zones and maintain high-quality technical documentation.
• Participate in on-call rotation for owned components, focusing on detection efficacy and real-time scoring systems.

Skills

Communication skills

Independent work

Cross-functional collaboration

Experimental mindset

Numerical computing

Education

Bachelor's degree in Computer Science or related field

Tools

Python

Go

AWS

Spark

Databricks

Job description

Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. This highly applied role emphasizes building, iterating, and delivering practical ML solutions from data wrangling to production deployment.

You will translate research ideas into robust systems that improve detection accuracy and scale with customer impact. The role requires strong Python/Go experience, production ML background, and ability to collaborate across distributed teams.