Senior Research Engineer - AI Security

Microsoft Microsoft · Big Tech · Redmond, WA +2 · Software Engineering

Senior Research Engineer focused on AI security for Microsoft Copilot, designing and operating ML systems to identify, evaluate, and mitigate AI security threats. This involves building datasets, training detection models, integrating defenses, and developing evaluation frameworks for agentic AI.

What you'd actually do

  1. 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.
  2. Develop adaptive detection and policy models that are capable of learning from evolving attacker behavior to offer durable protection against emerging AI security threats.
  3. Build and own evaluation frameworks for AI security, including adversarial testing, red‑teaming support, and continuous robustness measurement across real Copilot scenarios.
  4. Define success metrics and conduct rigorous experimentation to quantify security effectiveness, adversarial robustness, precision/recall tradeoffs, and user experience impact.
  5. Partner with security and engineering teams to integrate ML defenses into secure orchestration frameworks that govern agent delegation, tool calling, and action execution.

Skills

Required

  • 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
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • 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
  • 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
  • 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.

What the JD emphasized

  • production-scale ML systems
  • agentic workflow abuse
  • detection and policy models
  • evaluation frameworks for AI security
  • adversarial testing
  • red‑teaming support
  • continuous robustness measurement
  • agent delegation
  • tool calling
  • action execution
  • AI safety
  • trust
  • security adjacent ML problems
  • prompt injection
  • abuse detection
  • adversarial ML
  • security-sensitive production environments

Other signals

  • AI security
  • adversarial testing
  • detection and classification models
  • production ML systems
  • agentic workflow abuse