ML Engineer I: Misdirected Email Detection (MED)

Abnormal; United States; On-site; USD 90,000 - 130,000; Full time

Job summary

Abnormal AI is seeking a Machine Learning Engineer - I to join the Misdirected Email Detection team. This highly applied role focuses on building end-to-end ML solutions, feature engineering, and production deployment to prevent misdirected outbound emails at scale.

You will collaborate with product, tech leads, and engineers to drive GA launches, run rigorous experiments, and monitor system performance, delivering measurable customer impact in real-world environments.

Qualifications

• BS degree in Computer Science, ML, Information Systems, or related field.
• At least 1+ years building and operating applied ML features in production.
• Experience delivering end-to-end ML systems with data wrangling, feature engineering, model training, and deployment.

Responsibilities

• Own the full ML lifecycle for Misdirected Email, including data wrangling, feature engineering, model training, deployment, and monitoring.
• Run rigorous experiments and evaluations (offline metrics, online A/B tests, post-launch monitoring).
• Collaborate across Product, Tech Lead, and engineering to align deliverables with roadmap milestones.
• Maintain high-quality technical documentation and share knowledge across time zones.
• Participate in on-call rotation for detection components with focus on efficacy and reliability.

Skills

Applied ML production

End-to-end ML systems

Data wrangling (text & structured)

Offline/online evaluation

Experimentation & A/B testing

Cross-functional collaboration

Education

BS degree in Computer Science / ML / Info Systems

Tools

Python

Go

AWS

Spark

Databricks

Job description

Abnormal AI is seeking a Machine Learning Engineer - I to join the Misdirected Email Detection team. This highly applied role focuses on building end-to-end ML solutions, feature engineering, and production deployment to prevent misdirected outbound emails at scale.

You will collaborate with product, tech leads, and engineers to drive GA launches, run rigorous experiments, and monitor system performance, delivering measurable customer impact in real-world environments.