Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence

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, and human judgments
• Use uncertainty, confidence, severity, and business impact to determine when to automate, gather additional evidence, invoke a more capable system, abstain, or escalate to expert review
• Translate threat models and adversarial insights into data strategies, learning objectives, model architectures, agent capabilities, and evaluation plans
• Advance the training, post-training, and evaluation of agents that use tools and evidence to investigate complex cases and produce grounded outcomes
• Address challenging learning settings involving distribution shift, sparse or delayed labels, noisy supervision, class imbalance, selective observation, and adaptive adversaries
• Translate scientific advances into reliable, efficient, and measurable production capabilities across Microsoft Advertising
• Provide technical leadership, mentor scientists, and influence the long-term architecture of AI-driven trust and safety systems

Requirements

• Bachelor’s, Master’s, or Doctorate degree in Computer Science, Mathematics, Statistics, Electrical Engineering, Operations Research, or a related quantitative field, with relevant industry or research experience
• Strong foundation in probability, statistics, linear algebra, optimization, numerical methods, experimental design, and statistical decision theory
• Deep expertise in modern machine learning, including foundation or representation learning, behavioral and temporal modeling, anomaly detection
• Proven experience in post-training and evaluating large-scale models (xxx B param)
• Experience modeling uncertainty in production decision systems
• Ability to model threat and abuse scenarios
• Strong programming skills in Python and experience with frameworks such as PyTorch, JAX, TensorFlow, or equivalent technologies
• Proven ability to take scientific ideas from formulation through experimentation, production deployment, and measurable impact
• Demonstrated technical leadership through scientific direction, architecture, mentorship, and influence across science, engineering, product, and security teams

Tech stack