Overview
Machine Learning Engineer I for Abnormal AI’s Misdirected Email Detection (MED) team, building end-to-end ML solutions to detect and block misdirected outbound emails.
What you'll do
- Own the full ML lifecycle for misdirected email detection, from data wrangling to monitoring.
- Develop and refine features, run experiments, and improve detection reliability.
- Conduct offline metrics, online A/B testing, and post-launch monitoring with threshold setting and error analysis.
- Deploy and maintain production models and detection systems, including drift/performance monitoring and rollback guardrails.
- Collaborate with Product Manager, Tech Lead, and engineering stakeholders to match technical deliverables to roadmap milestones.
- Participate in a shared on-call rotation focused on detection efficacy and real-time scoring systems.
- Investigate efficacy-related alerts and high-visibility false positives/false negatives.
What you'll need
- BS degree in Computer Science, Machine Learning, Artificial Intelligence, Information Systems, or a related quantitative field.
- 1+ years building and operating applied ML features in production systems.
- Experience contributing to end-to-end ML systems (data wrangling, feature engineering, training, evaluation, deployment, and monitoring).
- Ability to implement and reason about algorithms and use numerical computing for signal combination/averaging.
- Experience interrogating production data, identifying behavioral/trend shifts, and launching targeted experiments.
- Understanding of online vs offline pipelines and labeling/workflow concepts for safe, scalable deployments.
- Experience running offline metrics, online A/B tests, threshold setting, and monitoring drift/performance.
- Strong written and asynchronous communication skills.
- Ability to work independently and across distributed, cross-functional teams.
Posted yesterday
1+ Years
Negotiable
