Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence

About the role

We are looking for a Principal Applied Scientist with a strong foundation in mathematics, statistics, and core machine learning to advance: Foundation models for behavioral, content, entity, and risk understanding. Anomaly detection and threat modeling for new and evolving abuse patterns. Decision uncertainty modeling across models, agents, workflows, and human review.

What you'll do

• Define and lead scientific initiatives in one or more areas eg foundation models, behavioral modeling, anomaly detection, threat modeling, agentic systems
• Develop scalable learning systems that understand entities, content, relationships, and behavior over time while identifying known, emerging, and previously unseen risks
• Develop methods to model and propagate uncertainty across individual models, model cascades, agent trajectories, retrieved evidence, automated decisions
• Use uncertainty, confidence, severity, and business impact to determine when to automate, gather additional evidence, invoke a more capable system, abstain
• Translate threat models and adversarial insights into data strategies, learning objectives, model architectures, agent capabilities, and evaluation plans

What you need

• Machine learning
• Python
• Statistics
• PyTorch
• TensorFlow
• PhD or B.Tech or B.E. or B.Sc or BCA or M.Tech or MCA