Security Software Engineer II

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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 looking for a Security Software Engineer with hands on knowledge with proven track record in deploying large scale models in production and AI/MLOps. Are you excited about AI safety? Do you want to develop automation that red teams can use to exploit security vulnerabilities in Microsoft's largest AI systems, impacting millions of users? We are looking for a Security Software Engineer to join Microsoft's PyRIT Team (https://github.com/Azure/PyRIT), where you'll create software that emulates real-world attacks against our AI products. As part of the AI Red Team—an interdisciplinary group of red teamers, adversarial Machine Learning (ML) researchers, and software developers—you’ll help identify risks in Microsoft’s most impactful AI investments.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required/minimum qualifications

• Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• 2+ years of experience in machine learning, MLOps/AIOPs
• 1+ years of experience with Azure Machine Learning, Azure Fabric, Synapse, or similar platforms.

• Proven track record of deploying large-scale machine learning systems in production.
• Experience with cloud platforms (Azure preferred) and infrastructure as code (e.g., Terraform, ARM templates).
• Experience optimizing ML models for performance and scalability in production environments.
• Hands-on experience in building and deploying ML models in a cloud environment (preferably Azure).
• Experience with containerization (Docker, Kubernetes) and microservices architecture.
• Basic understanding of model monitoring, retraining, and model governance practices.

Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $98,300 - $193,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $127,200 - $208,800 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until May 5, 2025.

#MSFTSecurity #MSECAIR #airedteam #AML #python #Azure #AzureMachineLearning

• Design, implement, and support AI-driven adversary emulation tooling
• Support partner development teams and the open source community in contributing to our services
• Build deep relationships with peers through design and code reviews, and peer mentoring
• Analyze emerging attack techniques from red teams, adversarial researchers, and external threat actors, leveraging these insights to develop and refine advanced security tooling.
• Develop and maintain MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).
• Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.