# Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence **Company**: Microsoft **Location**: Bengaluru, India **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Ticker**: MSFT **Wikidata**: https://www.wikidata.org/wiki/Q2283 **Apply**: https://microsoft.ai/job/principal-applied-scientist-foundation-models-agents-decision-intelligence/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_d037ba5f-cd9 ## Description 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. ## Skills ### Required - Python - PyTorch - JAX - TensorFlow - machine learning - statistics - linear algebra - optimization - numerical methods - experimental design - statistical decision theory - foundation models - behavioral modeling - anomaly detection - threat modeling - agentic systems ### Nice to have - tool-using agents - retrieval - agent post-training - reward modeling - trajectory evaluation - trust and safety - fraud - abuse - cybersecurity - moderation - account integrity - policy enforcement - temporal data - multimodal data - heterogeneous data - graph-structured data --- Source: [Apply at microsoft.ai](https://microsoft.ai/job/principal-applied-scientist-foundation-models-agents-decision-intelligence/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)