Abnormal - Machine Learning Engineer II Behavioral Security Products
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Requirements
• Proven experience as a Machine Learning Engineer or similar role in a commercial environment (3+ years). • Knowledge of machine learning algorithms, statistics, and predictive modeling. • Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally. pytorch/tensorflow. • Awareness of machine learning operations (MLOps) and productionization of ML models best practise.. • Familiarity with building data and metric generation pipelines, using tools like SQL or Spark, to answer business questions and assess system efficacy. • Ability to communicate technical ideas in a clear, non-technical manner. • Familiarity with LLMs • Previous experience in Cybersecurity • Previous experience with Airflow or similar ML pipeline orchestration tools • Experience with large scale ML system and data infrastructure • Previous experience in behavioural modeling techniques • PhD or equivalent proven experience in ML research • Familiarity with cloud computing platforms (AWS, Azure
Responsibilities
• Contribute to the development of machine learning algorithms and models for behavioral modeling and cybersecurity attack detection. • Work with cross-functional teams to understand requirements and translate them into effective machine learning solutions. • Conduct exploratory data analysis, feature engineering, model development and evaluation. • Work with infrastructure & product engineers to productionize models and new ML-based features • Monitor and improve production models through feature engineering, rules, and ML modeling as part of a team effort. • Participate in code reviews to ensure the quality and maintainability of ML systems. • Stay updated on the latest research in the field of machine learning, data science, and AI. • Adopt and contribute to the development of machine learning best practices within the organization.
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