Design, deploy, and operate production-scale ML systems that protect Copilot experiences, ensuring high reliability, performance, and security at global scale, targeting threats such as prompt injections, adversarial inputs, and agentic workflow abuse. Develop adaptive detection and policy models that are capable of learning from evolving attacker behavior to offer durable protection against emerging AI security threats. Build and own evaluation frameworks for AI security, including adversarial testing, red‑teaming support, and continuous robustness measurement across real Copilot scenarios. Define success metrics and conduct rigorous experimentation to quantify security effectiveness, adversarial robustness, precision/recall tradeoffs, and user experience impact. Partner with security and engineering teams to integrate ML defenses into secure orchestration frameworks that govern agent delegation, tool calling, and action execution. Monitor and analyze telemetry to improve model performance, reduce false positives/negatives, and guide iterative defense improvements. On-call Engineering Duties Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 4+ years of hands-on experience building and shipping machine learning, detection, ranking, classification, or data-driven decision systems in production. Experience building systems related to adversarial testing, evaluation frameworks, telemetry/observability pipelines, or risk‑measurement infrastructure. Solid foundation in ML fundamentals, including classification, anomaly detection, representation learning, and model evaluation. Experience designing end to end ML pipelines: data collection, training, evaluation, deployment, and monitoring. Understanding of agentic AI risks (e.g., jailbreaks, prompt injection, toolchain misuse) and threat‑driven engineering. Experience working on AI safety, trust, or security adjacent ML problems, including prompt injection, abuse detection, or adversarial ML. Familiarity with distributed systems, cloud-based services, secure system design patterns, or security-sensitive production environments. Ability to clearly communicate complex ML and security concepts to engineering and non ML stakeholders.