About The Position

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. The team builds and operates the large-scale AI training and adaptation engines that power Microsoft Security products, turning cutting-edge research into dependable, production-ready capabilities.

Requirements

  • Strong coding skills.
  • Experience in AI model development.
  • Knowledge of privacy-aware data curation.
  • Familiarity with reinforcement learning techniques.
  • Ability to design benchmarks and quality gates.
  • Experience in mentoring and leading teams.

Nice To Haves

  • Experience with security technologies.
  • Familiarity with compliance frameworks.
  • Background in large-scale AI systems.

Responsibilities

  • Lead end-to-end model development for security scenarios.
  • Conduct privacy-aware data curation.
  • Implement continual pretraining and task-focused fine-tuning.
  • Utilize reinforcement learning and rigorous evaluation.
  • Deepen model reasoning and tool-use skills.
  • Embed responsible AI and compliance into every stage of the workflow.
  • Partner closely with engineering and product teams to translate innovations into shipped experiences.
  • Design objective benchmarks and quality gates.
  • Mentor scientists and engineers to scale results across globally distributed teams.
  • Combine strong coding and experimentation with a systems mindset to accelerate iteration cycles.

Benefits

  • Growth mindset culture.
  • Opportunities for innovation.
  • Collaborative work environment.
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