Senior Machine Learning Engineer

• Design, train, and deploy ML‑based defenses for threats such as prompt injection, adversarial inputs, and abuse of agentic workflows.
• Develop adaptive detection and policy models that learn from evolving attacker behavior rather than relying solely on static rules or signatures.
• Build and own evaluation frameworks for AI security, including adversarial testing, red‑teaming support, and continuous robustness measurement across real Copilot scenarios.
• Partner with security and engineering teams to integrate ML defenses into secure orchestration frameworks that govern agent delegation, tool calling, and action execution.
• Apply ML to encode security “common sense” and judgment into AI responses, balancing usefulness, safety, and user intent.
• Monitor and analyze telemetry to improve model performance, reduce false positives/negatives, and guide iterative defense improvements.
• Collaborate cross‑functionally with product, privacy, and AI platform teams to land agentic security patterns across Copilot and MAI.
• Document and share applied ML security techniques, helping establish best practices for secure agentic AI across Microsoft.

• 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 OR equivalent experience.
• 4+ years of hands‑on experience building and shipping machine learning systems in production.
• Solid foundation in ML fundamentals, including classification, anomaly detection, representation learning, and model evaluation.
• Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow).
• Experience designing end‑to‑end ML pipelines: data collection, training, evaluation, deployment, and monitoring.
• Ability to reason about adversarial behavior, threat models, and failure modes in AI/ML systems.
• Experience working on AI safety, trust, or security‑adjacent ML problems, including prompt injection, abuse detection, or adversarial ML.
• Familiarity with agentic or LLM‑based systems, including tool calling, multi‑step reasoning, or orchestration flows.
• Experience building ML evaluation and observability systems for real‑world AI behavior (e.g., adversarial testing, red‑team loops, robustness metrics).
• Exposure to distributed ML systems, large‑scale data processing, or model serving in cloud environments.
• Ability to clearly communicate complex ML and security concepts to engineering and non‑ML stakeholders.

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.