Machine Learning Engineer, Moderation

• ^{Design, develop and maintain a robust and scalable data management pipeline and set up standards for versioning and data quality control. The pipeline should be able to handle data relabeling requests due to content policy changes.}
• ^{Build a pipeline for automated model training, evaluation and deployment, including active learning process, routines for calibration and validation data refresh etc.}
• ^{Work closely with stakeholders from product, engineering, content policy on a long-term improvement over the moderation models, for both external release and internal use cases across a variety of projects on model safety.}
• ^{Research on the latest techniques and methods in deep learning and natural language processing to improve the moderation model across a collection of unwanted content categories.}
• ^{Experiment on data augmentation and data generation methods to enhance the diversity and quality of training data.}
• ^{Experiment and design an effective red-teaming pipeline to examine the robustness of the model and identify areas for future improvement.}
• ^{Conduct open-ended research to improve the quality of collected data, including but not limited to, semi-supervised learning and human-in-the-loop machine learning.}

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• ^{Have 3+ years industry experience as a Machine Learning Engineer or Software Engineer, working on building data pipelines, training and deploying machine learning models in production on a daily basis.}
• ^{Care deeply about AI safety and passionate about building the best deep learning empowered moderation model to effectively detect unwanted content.}
• ^{Have a strong belief in the criticality of high-quality data and are highly motivated to work with the associated challenges.}
• ^{Have experience working in large distributed systems, deep learning or/and natural language processing is a big plus.}
• ^{Love working with a team.}

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