Descripción
Accountabilities:
- You will contribute across the machine learning lifecycle and production engineering stack, ensuring that email detection models are accurate, efficient, and reliable in real-world hosting environments. Your responsibilities will include: Model Training and Deployment: Train, evaluate, and release spam-detection models, including building and maintaining training corpora, selecting decision thresholds, analyzing precision and recall, and determining whether models are ready for production.
- Production ML Engineering: Develop the production components of a new email-filtering engine in Go, including the inference service, mail-filter integrations, tokenization, and streaming message parsing.
- Detection Quality Improvement: Investigate customer-reported spam and phishing incidents, trace detection failures, and implement appropriate fixes through rules, dataset improvements, or model retraining.
- Model Performance Optimization: Balance detection rates against false-positive risks, ensuring legitimate messages are not unnecessarily blocked while malicious content is effectively identified.
- Training Infrastructure and Data Tooling: Maintain the corpus management tools and training infrastructure required to support reliable experimentation and model releases.
- Code and Model Reviews: Review and approve classifier, rule, and model changes to improve quality, share technical knowledge, and ensure releases can proceed without depending on a single engineer.
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