Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence

Microsoft Advertising is building the next generation of AI systems for understanding advertiser behavior, detecting anomalies, and emerging threats.

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.
• Tool-using agents that investigate cases, gather evidence, and support automated and human decisions.
• Rigorous evaluation of models, agents, and end-to-end decision systems.

You will work with large-scale behavioral, multimodal, temporal, and relational data to build capabilities that generalize across products, markets, policies, and changing adversarial environments.

This is a hands-on scientific role with end-to-end ownership from problem formulation and model development through large-scale training, evaluation, productionization, and measurable product impact.

Responsibilities

• Define and lead scientific initiatives in one or more areas, e.g., 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.

Qualifications

• 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.
• 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.

Preferred Qualifications

• Experience with tool-using agents, retrieval, agent post-training, reward modeling, or trajectory evaluation.
• Experience in trust and safety, fraud, abuse, cybersecurity, moderation, account integrity, or policy enforcement.
• Experience working with temporal, multimodal, heterogeneous, or graph-structured data.
• Strong publication or production track record in machine learning, agents, anomaly detection, probabilistic modeling, adversarial ML, multimodal learning, or trust and safety.