Director, Model Post-Training and Agentic Research

CrowdStrike

Director of Model Post-Training leading AI research at CrowdStrike. Overseeing security-domain AI pipelines and managing experimental work and research priorities.

Posted 6/11/2026full-timeRemote • 🇺🇸 United StatesLead💰 $195,000 - $290,000 per yearWebsite

About the role

Key responsibilities & impact

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 CrowdStrike's credibility as a research-first organization in this space.

Requirements

What you’ll need

MS or PhD 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.

Benefits

Comp & perks

Market leader in compensation and equity awards
Comprehensive physical and mental wellness programs
Competitive vacation and holidays for recharge
Paid parental and adoption leaves
Professional development opportunities for all employees regardless of level or role
Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
Vibrant office culture with world class amenities
Great Place to Work Certified™ across the globe

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learninglarge language modelsSFT data pipelinesRLHFRLAIFPPOreward model designagentic system harnessesevaluation protocolscontext management

Soft Skills

leadershipresearch prioritizationevaluation instinctscollaborationcommunicationproblem-solvingfast iterationteam growthtechnical contributioninterpersonal skills

Certifications

MS in computer sciencePhD in computer sciencePhD in machine learningPhD in quantitative discipline