Director, Model Post-Training and Agentic Research (Remote)

CrowdStrike

Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$195,000–$290,000 / yr
Posted
5 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $236k
This role $242k
$181k most similar roles pay here $302k

This role pays more than 51% of similar roles. Most pay $211,200–$261,000 — the shaded band above. At the midpoint, this role pays about $242k versus about $236k for comparable roles.

Based on 240 similar postings.

Employer

About CrowdStrike

CrowdStrike is a leading American cybersecurity technology firm, specializing in cloud-native endpoint protection, threat intelligence, and incident response.

CrowdStrike currently has 27 open roles on FindRole.

Listed pay typically runs $125,000–$180,000 across 27 roles with salary data.

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View all roles at CrowdStrike

At a glance

TL;DR · Director, Model Post-Training and Agentic Research (Remote)

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Join our dynamic security research team as an experienced ML scientist or engineer to lead the development of advanced post-training pipelines for AI in cybersecurity. You will own the full stack from supervised fine-tuning and reward modeling to reinforcement learning environments, setting technical direction and driving high-velocity research cycles. Your responsibilities include designing robust evaluation methodologies, building agent harnesses that manage memory and context, and integrating your work with broader model development efforts. Ideal candidates have a deep background in large language models, post-training techniques like RLHF/RLAIF, and hands-on experience with agentic system design for complex security tasks. Familiarity with cybersecurity workflows and published research in related fields is a plus.

Skills

Python TensorFlow PyTorch Kubernetes Docker CI/CD PostgreSQL AWS Git Jupyter Notebook Pandas Scikit-learn Numpy Matplotlib Seaborn OpenAI Gym Hugging Face Transformers LangChain RFRL SFT

What you'll do

  • Own and drive the full post-training pipeline for security-domain AI, including SFT, RLHF/RLAIF, and reward modeling.
  • Design and build agent-RL training environments simulating realistic cyber workflows with multi-step tasks and long-horizon planning.
  • Develop evaluation methodology to assess harness behavior, tool-use reliability, and end-to-end task completion in security workflows.
  • Lead the design of agent harnesses that run on trained models, including scaffolding architecture and memory management systems.
  • Recruit, develop, and retain a high-density team of research scientists and ML engineers while setting technical standards through personal contributions.
  • Partner with other teams to ensure post-training work integrates seamlessly with broader model development processes.

What we're looking for

  • MS or PhD in computer science, machine learning, or related quantitative field.
  • 8+ years experience in ML research or engineering with expertise in large language model post-training.
  • Hands-on experience designing and building agentic system harnesses for LLM-based agents.
  • Strong evaluation methodology skills to measure genuine capability improvement in models.
  • Proven ability to lead high-velocity research programs and grow technical teams.

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