{"id":2759292,"url":"https://alion.io/job/microsoft-principal-applied-scientist-foundation-models-agents-decision-intelligence","title":"Principal Applied Scientist- Foundation Models, Agents & Decision Intelligence","company":{"id":18,"name":"Microsoft","domain":"microsoft.com","url":"https://alion.io/company/microsoft","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":80,"open_postings":292,"ghost_share":0,"stale_share":0.791,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":76000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Threat Modeling","optional":false},{"name":"Reward Modeling","optional":true}],"status":"live","first_seen_at":"2026-07-22T06:12:03Z","employer_posted_date":"2026-07-24","last_verified_at":"2026-10-23T23:35:48Z","board_verified":true,"closed_at":null,"days_open":93,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":92},"description":"OverviewAbout the Role\nMicrosoft Advertising is building the next generation of AI systems for understanding advertiser behavior, detecting anomalies and emerging threats.\nWe are looking for a Principal Applied Scientist with a strong foundation in mathematics, statistics, and core machine learning to advance:\nFoundation models for behavioral, content, entity, and risk understanding.\nAnomaly detection and threat modeling for new and evolving abuse patterns.\nDecision uncertainty modeling across models, agents, workflows, and human review.\nTool-using agents that investigate cases, gather evidence, and support automated and human decisions.\nRigorous evaluation of models, agents, and end-to-end decision systems.\nYou will work with large-scale behavioral, multimodal, temporal, and relational data to build capabilities that generalize across products, markets, policies, and changing adversarial environments.\nThis 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\nResponsibilities\nDefine and lead scientific initiatives in one or more areas eg foundation models, behavioral modeling, anomaly detection, threat modeling, agentic systems.\nDevelop scalable learning systems that understand entities, content, relationships, and behavior over time while identifying known, emerging, and previously unseen risks.\nDevelop methods to model and propagate uncertainty across individual models, model cascades, agent trajectories, retrieved evidence, automated decisions, and human judgments.\nUse 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.\nTranslate threat models and adversarial insights into data strategies, learning objectives, model architectures, agent capabilities, and evaluation plans.\nAdvance the training, post-training, and evaluation of agents that use tools and evidence to investigate complex cases and produce grounded outcomes.\nAddress challenging learning settings involving distribution shift, sparse or delayed labels, noisy supervision, class imbalance, selective observation, and adaptive adversaries.\nTranslate scientific advances into reliable, efficient, and measurable production capabilities across Microsoft Advertising.\nProvide technical leadership, mentor scientists, and influence the long-term architecture of AI-driven trust and safety systems.\nQualifications\nBachelor’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 .\nStrong foundation in probability, statistics, linear algebra, optimization, numerical methods, experimental design, and statistical decision theory.\nDeep expertise in modern machine learning, including foundation or representation learning, behavioral and temporal modeling, anomaly detection.\nProven experience in post-training and evaluating large-scale models (xxx B param)\nExperience modeling uncertainty in production decision systems.\nAbility to model threat and abuse scenarious.\nStrong programming skills in Python and experience with frameworks such as PyTorch, JAX, TensorFlow, or equivalent technologies.\nProven ability to take scientific ideas from formulation through experimentation, production deployment, and measurable impact.\nDemonstrated technical leadership through scientific direction, architecture, mentorship, and influence across science, engineering, product, and security teams.\nPreferred Qualifications\nExperience with tool-using agents, retrieval, agent post-training, reward modeling, or trajectory evaluation.\nExperience in trust and safety, fraud, abuse, cybersecurity, moderation, account integrity, or policy enforcement.\nExperience working with temporal, multimodal, heterogeneous, or graph-structured data.\nStrong publication or production track record in machine learning, agents, anomaly detection, probabilistic modeling, adversarial ML, multimodal learning, or trust and safety.\nThis position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.\nMicrosoft 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. 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