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Microsoft Defender for Endpoint (MDE) is a product for preventative protection, post-breach detection, automated investigation, and response. Our team, the core machine learning and data science team, is a cross-discipline team responsible for building ML, LLM, and automation solutions that defend over a billion end users and enterprises from cybersecurity attacks through Microsoft Defender AntiVirus, Microsoft Defender Endpoint Detection and Response, and Network Protection products. We are a mix of machine learning engineers, data scientists, data engineers, and security researchers who develop big data pipelines, run experiments, and deploy our protection to production to protect customers at scale.
Job Responsibility:
Investigate attacks through threat hunting on top of product telemetry - identifying protection gaps and opportunities for systems to better protect our customers
Experiment with and apply large language models and agentic systems to protect our customers and improve our internal systems
Propose, design, experiment, and implement machine learning and automation designs to protect our customers
Collaborate closely with engineering and product teams to design security sensors, validate protection concepts, and measure effectiveness using data-driven methodologies
Requirements:
Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 1+ year(s) experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection
OR Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 2+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection
OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements
Microsoft Cloud Background Check
1+ years experience developing systems with Large Language Models or Machine Learning (eg Logistic Regression, LightGBM, XGBoost, PyTorch, BERT, or similar)
1+ years of experience with large-scale data, utilizing either distributed data processing frameworks (e.g., Apache Spark, Hadoop), real-time data streaming platforms (e.g., Kafka), or query languages like SQL and KQL