Applied Scientist II, Perimeter Protection Applied Science

Key job responsibilities

• Design, develop, and evaluate ML models and algorithms for threat detection, anomaly detection, and mitigation of evolving cyber threats including DDoS attacks, bot activity, and web application exploits.
• Explore and apply large language models, generative AI, and agentic AI approaches to security challenges such as automated threat analysis, intelligent mitigation, and adaptive defense systems.
• Implement end-to-end ML solutions — from data exploration and feature engineering through model training, evaluation, and deployment into production systems.
• Analyze large-scale datasets to uncover patterns, identify emerging threat vectors, and translate findings into effective ML-based security solutions.
• Build and maintain data pipelines and model training workflows that support rapid experimentation and reliable production performance.
• Collaborate with software engineers to integrate ML models into low-latency, high-throughput security systems at cloud scale.
• Design and run experiments to validate model performance, measure impact, and iterate on approaches using rigorous scientific methodology.
• Stay current with recent advances in AI/ML — including LLMs, generative AI, and agentic systems — and cybersecurity research, applying relevant techniques to improve detection and protection capabilities.
• Contribute to design reviews, and knowledge sharing.
• Propose ideas and identify opportunities to improve existing systems within the team's scientific roadmap.

Basic Qualifications

• 2+ years of building models for business applications experience
• PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow

Preferred Qualifications

• PhD in computer science, computer engineering, or related field
• Experience in designing experiments and statistical analysis of results
• Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience applying theoretical models in an applied environment
• Publications at top-tier peer-reviewed conferences or journals