Machine Learning Engineer 1
New
A
Abnormal AIEmail security
Remote - Singapore, Communicate effectively across time zonesFull-TimeEntry
Salary not disclosed
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Job Details
- Experience
- 1+ years building and operating applied ML features in production systems.
- Required Skills
- AWSPythonMachine LearningGoSparkA/B testingDatabricks
Requirements
- Hold a BS degree in computer science, machine learning, artificial intelligence, information systems, or a related engineering or quantitative field.
- Have 1+ years of experience building and operating applied ML features in production systems.
- Have experience contributing to end-to-end ML systems, including data wrangling with text and structured data, feature engineering, model selection, training, evaluation, production deployment, and monitoring.
- Be able to implement and reason about algorithms, develop features, combine signals, and apply numerical computing.
- Have experience interrogating production data, identifying behavioral or trend shifts, and launching targeted experiments.
- Understand online and offline pipelines, data tables, and labeling workflows for safe, scalable model deployments.
- Have experience running offline metrics and online A/B tests, setting thresholds, and monitoring drift and performance with guardrails and rollback strategies.
- Have strong written and asynchronous communication skills and be able to work independently across distributed, cross-functional teams.
- Experience with Python, Go, AWS, Spark, or Databricks is a plus.
- Experience in email security, DLP, misdirected email prevention, or customer-focused ML deployments is a plus.
Responsibilities
- Partner with product, technical, and engineering stakeholders to align deliverables with roadmap milestones and support general availability launches.
- Own the end-to-end machine learning lifecycle for misdirected email detection, from data wrangling and feature engineering through deployment and monitoring.
- Run offline evaluations and online A/B tests, set thresholds, and conduct targeted error analysis.
- Monitor model performance and production behavior to identify shifts and prevent regressions.
- Iterate on detection models and features to improve reliability and customer impact.
- Maintain technical documentation and contribute to shared team knowledge.
- Participate in the shared on-call rotation for owned components, addressing efficacy alerts and false positives or false negatives.
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