Senior Machine Learning Engineer, Censys

• Bachelor's degree in Computer Science, Data Science, Engineering, or other technical discipline (or equivalent professional experience)
• 5+ years of experience in machine learning, NLP, or data science roles
• Strong proficiency in Python, Go, and machine learning libraries such as TensorFlow or PyTorch
• Experience with data processing frameworks like Kafka, Spark, and real-time data streaming technologies
• Deep understanding of model training, feature engineering, and performance optimization
• Experience working with cloud platforms (AWS, GCP, etc.) and containerization (Docker, Kubernetes)
• Expertise in large-scale data analysis, with experience in anomaly detection and predictive modeling
• Ability to transform complex datasets into understandable, actionable insights for end-users

Desirable

• Censys is looking for a Senior Machine Learning Engineer to join our team and help us derive valuable insights from internet security datasets
• In this role, you will develop and maintain machine learning models that process large volumes of data, identifying patterns, anomalies, and trends that inform our customers about significant security events
• You’ll work closely with data scientists, engineers, and product teams to build scalable systems that enhance how customers interact with and understand our data
• You will design machine learning systems that not only process and analyze data but also bring critical insights to the surface in a way that empowers decision-making
• Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals
• Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights
• Collaborate with cross-functional teams to design data pipelines that can efficiently process terabytes of raw internet security data
• Drive projects focused on feature engineering, model performance optimization, and contextual analysis of security-related data
• Communicate key findings from data, enabling customers to better understand security threats and mitigate risks