Applied Scientist

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 looking to hire an Applied Scientist. We are 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.

1. • Design, develop, and maintain the machine learning platform that powers cybersecurity protection machine learning models in our products and services.
   • Collaborate with others to identify opportunities to optimize data tools used to transform, manage, and access data across teams. Ensures the effectiveness and placement of performance monitoring protocols across multiple data pipelines.
   • 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 designs to protect our customers.

Required Qualifications

• • Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND relevant internship experience OR Master's Degree in Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Preferred Qualifications

• • 1+ years of experience working on bringing machine learning pipelines to production systems.
  • 1+ years of experience developing large-scale data pipelines, 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.
  • 1+ years of experience with Large Language Models (LLMs).
  • 1+ years of experience with classical machine learning application (eg Logistic Regression, LightGBM, XGBoost, etc) or custom deep-learning approaches using PyTorch or similar technology.
  • Cybersecurity experience.
  • 1+ years of experience developing on at least one cloud platform (e.g., Azure, AWS, GCP).
  • 1+ years of experience in MLOps, including model deployment, monitoring, version control, and continuous integration/continuous delivery (CI/CD) pipelines for machine learning projects, using tools such as Azure Machine Learning, MLflow, Kubeflow, TensorFlow Serving, or similar.

Applied Sciences IC2 - The typical base pay range for this role across the U.S. is USD $84,200 - $165,200 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 $109,000 - $180,400 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

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.