Director, Model Post-Training and Agentic Research; Remote

As a global leader in cybersecurity, Crowd Strike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn't changed - we're here to stop breaches, and we've redefined modern security with the world's most advanced AI-native platform. Our customers span all industries, and they count on Crowd Strike to keep their businesses running, their communities safe and their lives moving forward.

We're also a mission-driven company. We cultivate a culture that gives every Crowd Striker both the flexibility and autonomy to own their careers. We're always looking to add talented Crowd Strikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters?

The future of cybersecurity starts with you.

The security domain presents one of the richest and most consequential training signal environments in applied AI. It's adversarial by nature, grounded in real operational outcomes, and evolving faster than any static benchmark can capture. We're building the post-training and reinforcement learning capability to build the latest models and harnesses into security-specialized systems that reason, plan, and act across complex cyber workflows.

The person leading this work will be in the research, not just directing it.

In this role, you'll own the full post-training stack for security-domain AI (e.g., supervised fine-tuning, reward modeling, RLHF and RLAIF pipelines, and agent-RL environments) and the agentic research that sits on top of it. That means designing, building, and evaluating the harnesses that security agents actually run on (e.g., the scaffolding, tool-use interfaces, planning loops, memory and context management, and multi-step execution frameworks) that determine whether a trained model can operate reliably on complex security tasks.

Post-training and agent architecture are not separable problems in this work. The reward signal you design has to reflect what the harness can measure, and the harness has to be built to surface what training needs to optimize. You'll set the technical direction on both, and you'll be in the work on both.

You’ll lead a team of research scientists and engineers, but the team will look to your own work as the standard. The successful candidate shapes research priorities, keeps the team moving at high velocity across multiple training cycles per year, and elevates the quality of work by staying close enough to it to know what good actually looks like.

• Own and personally drive the full post-training pipeline for security-domain AI - SFT, RLHF/RLAIF, agent-RL, and reward modeling. Set research priorities and architectural direction, and lead experimental work on the hardest problems yourself rather than delegating them away. Design reward modeling methodology grounded in verified security outcomes rather than proxy signals, drawing on both human expert feedback and automated adversarial evaluation.
  
  Define data curation standards across sourcing, filtering, quality scoring, and domain weighting that drive measurable capability improvement.
• Build and maintain agent-RL training environments that simulate realistic cyber workflows (multi-step offensive and defensive tasks, tool use, and long-horizon planning) contributing directly to environment design and reward shaping. Lead the design and build of the agent harnesses that run on top of those trained models: scaffolding architecture, tool-calling interfaces, planning and reasoning loops, and memory and context management. Treat harness design with the same rigor as the training pipeline;
  
  these systems determine whether strong post-training translates into reliable, trustworthy behavior in the field.
• Develop and own evaluation methodology for the full agentic stack, not model capability in isolation, but harness behavior, tool-use reliability, planning coherence, and end-to-end task completion across realistic security workflows. Define the benchmarks, red-line tests, and measurement practices that give the team and the…