Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence

Job description

**Overview** **About the Role** 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 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. **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 (xxx B param) - Experience modeling uncertainty in production decision systems. - Ability to model threat and abuse scenarious. - 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. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. --- Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about [**requesting accommodations.**](https://careers.microsoft.com/v2/global/en/accessibility.html)