Join Microsoft AI and help shape the future of AI-powered products. This role focuses on building safe, trustworthy, and effective AI platforms, tackling challenges in trust and safety, fraud prevention, and risk management. You'll collaborate with diverse teams to deliver scalable solutions that protect users and drive sustainable business growth.

This is an opportunity to influence how Trust & Safety systems operate, manage risk, and leverage AI for improved outcomes. The role is within the Microsoft AI (MAI) organization, powering experiences across Copilot, Bing, Edge, and more. You will be part of a collaborative team focused on customer obsession and data-driven decision-making in a dynamic AI landscape.

Embrace ambiguity and complex problem-solving. This position is for a builder passionate about using data, automation, and operational excellence to enhance global Trust & Safety outcomes. Microsoft's mission is to empower every person and organization to achieve more, and our culture fosters growth mindset, innovation, and collaboration.

Partner with Policy, Engineering, Data Science, and Operations teams to create scalable policy solutions that enhance ecosystem safety, trust, and quality. Define and refine policy frameworks, reviewer guidelines, decision criteria, and operational playbooks for consistency and quality enforcement. Translate policy requirements into operational processes, product specifications, evaluation frameworks, and scalable enforcement mechanisms. Facilitate policy-to-model enablement by collaborating with engineering and data science teams to advance AI/ML-powered review, detection, and enforcement systems. Analyze labeling, evaluation, audit, and operational data to identify quality gaps, emerging risks, policy ambiguities, and areas for enhancement. Conduct hands-on labeling, content review, evaluation, and adjudication activities as needed to establish ground truth, validate methodologies, and calibrate quality standards. Develop quality metrics, evaluation methodologies, and feedback loops to continuously improve human review and automated decision-making systems. Collaborate with vendor and review operations teams to enhance annotation quality, reviewer effectiveness, operational efficiency, and policy comprehension. Lead cross-functional initiatives from problem definition through execution, balancing customer experience, operational needs, policy intent, and business objectives. Communicate insights, recommendations, and policy decisions effectively to both technical and non-technical stakeholders. Uphold Microsoft's Culture and Values.

A Bachelor's degree in a relevant field such as Business, Public Policy, Communications, Operations, Economics, Psychology, Social Sciences, Data Science, Analytics, or Computer Science, or equivalent practical experience is required. Possess a minimum of 3 years of experience in areas like Product Management, Program Management, Trust & Safety, Policy Operations, Risk Management, Fraud Prevention, Content Moderation, Marketplace Integrity, or related domains. Demonstrated experience in developing, interpreting, operationalizing, or enforcing policies, guidelines, standards, or business rules within complex operational environments. Exhibit strong analytical and problem-solving skills, with a proven ability to leverage data for decision-making, opportunity identification, and quality/operational improvements. Experience collaborating effectively across cross-functional teams, including Engineering, Data Science, Operations, Policy, and Business stakeholders. Familiarity with designing or managing review workflows, quality programs, labeling operations, evaluation frameworks, or large-scale operational processes. Possess strong communication, stakeholder management, and influencing skills, with the capacity to drive alignment across diverse teams. Acquaintance with AI/ML-powered systems and the critical role of human review, labeling, evaluation, and feedback loops in enhancing model quality and safety outcomes.

Preferred qualifications include experience in large-scale Trust & Safety, Marketplace Integrity, Fraud Prevention, Online Safety, Risk Management, or Compliance ecosystems. Experience designing and scaling human review, labeling, policy enforcement, quality assurance, or evaluation programs that support operational and product objectives. Experience supporting AI/ML model development through labeling strategies, ground-truth creation, adjudication frameworks, evaluation design, and feedback loops. Familiarity with Generative AI, Agentic AI, Large Language Models (LLMs), and human-in-the-loop systems used to improve safety, quality, and operational effectiveness. Ability to generate actionable insights from large-scale operational, quality, labeling, or enforcement datasets and translate findings into policy, process, tooling, or product improvements. Working knowledge of SQL, Power BI, Python, or similar analytics, reporting, and experimentation tools. Demonstrated success leading cross-functional initiatives involving Engineering, Data Science, Operations, Policy, and Business stakeholders. Proven ability to thrive in fast-paced, ambiguous environments while driving measurable customer and business impact through ownership, customer focus, and data-driven decision making.