Microsoft Principal Offensive AI Scientist 

United States 

14017101

Required/Minimum Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
• OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 6+ years of experience as a machine learning engineer, including designing and managing ML pipelines, building end-to-end systems from research ideas to functional MVPs, and prototyping solutions for real-world applications.
• 6+ years of experience in research areas such as generative AI, reinforcement learning, graph representation learning, or unstructured data analysis using LLMs and/or graph algorithms.
• 6+ years of relevant industry experience translating cutting-edge research into real-world impact.

Other Requirements

• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Additional or Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
• 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• 5+ years experience conducting research as part of a research program (in academic or industry settings).
• 3+ years experience developing and deploying live production systems, as part of a product team.
• 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping
• 4+ years of experience with Python and machine learning frameworks such as PyTorch or TensorFlow.
• Experience running or supporting red team campaigns or adversary simulation operations.
• Experience developing LLM-based agentic systems for security applications.
• Experience applying knowledge graphs to security use cases.
• Experience generating synthetic data and environments to train LLM-based AI agents.
• Experience in safety and ethical aspects of AI.
• Experience in technology transfer of applied research.
• Experience conducting high-quality research and publishing.
• Experience in working with large-scale datasets.
• Experience in applying machine learning to security and safety domains, such as malware detection, fraud prevention, or cyber-physical systems.
• Background in cyber security including knowledge of adversary tradecraft, emerging threats, or SOC operations.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Collaborate with security research teams and applied scientists to implement AI-driven techniques that address capability gaps and enable offensive capabilities in security operations.
• Design and refine workflows leveraging LLMs and agentic systems to empower autonomous agents in analyzing and operationalizing insights from complex security scenarios.
• Build and optimize data pipelines to process structured and unstructured data, enabling context extraction and integration with other efforts to operationalize insights.
• Support the generation of synthetic data and simulation environments to train and evaluate agentic capabilities in real-world security contexts.
• Fine-tune and optimize machine learning models for security-specific applications, ensuring seamless integration into security workflows.
• Apply graph science methods to capture complex relationships in security data to enhance the efficacy of autonomous agents.
• Help define metrics and frameworks for evaluating autonomous agent capabilities, driving continuous improvement and alignment with organizational goals.

Other

• Embody our and