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.

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 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 looking for a Machine Learning Engineer II to collaborate with a mix of other 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.

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.

Responsibilities

• Design, develop, and maintain the machine learning data 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.
• Propose, design, experiment, and implement machine learning designs to protect our customers.
• Experiment with and apply large language models and agentic systems to protect our customers and improve our internal systems.

Qualifications

Required/minimum qualifications

• Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience  OR Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
• 1+ years of experience in MLOps, for example, 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.
• 1+ years of research or industry experience developing and/or applying machine learning models, including experience with Large Language Models (LLMs).
• 1+ years of experience working 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.
• 1+ years of experience developing on at least one major cloud platform (e.g., Azure, AWS, GCP).

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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Additional or preferred qualifications

• Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience OR Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience
  • OR equivalent experience.
• 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
• Cybersecurity knowledge is a bonus

Applied Sciences IC3 - The typical base pay range for this role across Canada is CAD $85,000 - CAD $166,800 per year.

Find additional pay information here: https://careers.microsoft.com/v2/global/en/canada-pay-information.html

Microsoft will accept applications for the role until June 26, 2025.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

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