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Senior Software Engineer - AI Safety and Security

United States, Redmond 119800.00 - 234700.00 USD / Year · Job Posted January 26, 2026
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Job Description

Security represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end to end, simplified solutions. The Microsoft Security organization accelerates Microsoft’s mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers’ heterogeneous environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world. We are seeking an experienced Senior Software Engineer - AI Safety and Security to join a high impact team sitting at the intersection of cybersecurity and generative AI. As an engineer on the AI Safety and Security Platform team, you will develop and execute the technical strategy for internal platform capabilities, partnering closely with cross-functional teams across the company, to secure Microsoft’s flagship AI and agentic products. You will build infrastructure to enable sophisticated threat detection and forensic investigation, produce threat intelligence and insights, and accelerate response to safety and security incidents.

Job Responsibility

  • AI Logging and Observability: Develop company-wide logging strategies and implementations, enabling reliable attack mapping and automated detections
  • understand the architecture and pipelines for existing logging, data storage, and observability systems
  • and determine what additional infrastructure should be built
  • Detection Engineering: Partner with AI Incident Response and Threat Hunting teams to create novel detection capabilities
  • build infrastructure that enables meta-cognition, mechanistic interpretability, and anomaly detection, to identify patterns of attack signatures at scale
  • Threat Intelligence: Deliver data integrations across multiple data sources and platforms
  • serve diverse stakeholder needs for threat intelligence
  • partner with data science to operationalize pipelines that aggregate and correlate multi-source signals
  • deliver actionable insights, trend analyses, and automated reporting integrated with detection and response workflows
  • Mitigations: Partner with AI Red Team, applied science, and security research to implement mitigation strategies against emerging attack techniques
  • partner with product teams to demonstrate safe system architecture design

Requirements

  • 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
  • Demonstrated technical depth with LLM-based systems—prompts, system instructions, agents/tools, RAG, embeddings—and experience building or securing AI copilots or agent-based products
  • Demonstrated record of successfully operating in highly ambiguous environments and integrating solutions into varied environments
  • Experience with cybersecurity workflows (alerting, triage, investigation, threat hunting, incident response) and familiarity with frameworks like MITRE ATT&CK, NIST, or OWASP for LLM applications
  • Exceptional written and verbal skills
  • adept at articulating business needs and driving alignment across engineering, research, and security teams
  • Designing, building, and operating scalable, highly available cloud services or distributed systems on platforms such as Azure, AWS, GCP, or comparable cloud environments, with production ownership and CI/CD pipeline integration
  • Applying distributed systems concepts such as concurrency, conflict resolution, and consensus algorithms to build resilient and maintainable back-end architectures
  • Building systems with emphasis on reliability, durability, and operational efficiency, including experience with live site operations, incident response, and performance optimization

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