Director, Model Post-Training and Agentic Research

Responsibilities

• 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 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.
• 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.
• Partner closely with other teams to ensure post‑training and agentic work integrates cleanly with the broader model development loop.
• Contribute original research through publications, external presentations, and open‑source artifacts where appropriate, building Crowd Strike's credibility as a research‑first organization in this space.

Requirements

• MS or Ph D in computer science, machine learning, or a related quantitative discipline.
• 8+ years of experience in ML research or engineering, with meaningful depth in large language model post‑training.
• Hands‑on expertise across the modern post‑training stack, including SFT data pipelines, RLHF/RLAIF, PPO or similar RL algorithms applied to language models, and reward model design and training.
• Demonstrated experience designing or building agentic system harnesses for LLM‑based agents, including tool‑use frameworks, planning scaffolds, multi‑step execution environments, and context or memory management.
• Strong evaluation instincts: experience designing evaluation protocols that are resistant to overfitting, capable of measuring genuine capability improvement, and interpretable to both technical and non‑technical stakeholders.
• Track record of running high‑velocity research programs with disciplined tracking and fast iteration.
• Proven ability to lead and grow research teams while remaining a credible, active technical contributor.
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