Principal Security Researcher – AI (Cybersecurity LLM Post-Training, Evals, and Environments)

At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.

Your Career As a Principal AI Researcher, you will advance the cybersecurity capabilities of large language models and autonomous AI agents by combining security research, rigorous evaluation, and applied LLM post-training. You will develop high-quality security data, realistic training and evaluation environments, reliable graders, and post-training methods for complex cybersecurity tasks spanning vulnerability research, threat analysis, detection, investigation, remediation, secure coding, and autonomous security workflows. You will work across security research, machine learning, and research engineering to identify model capability gaps and translate real-world cybersecurity problems into measurable model improvements.

Design reproducible training and evaluation environments for complex cybersecurity tasks, including vulnerability research, threat analysis, detection, investigation, remediation, secure coding, and autonomous security workflows. Transform source-code repositories, vulnerabilities, security incidents, malware samples, threat intelligence, detection logic, patches, test harnesses, and security tools into structured tasks for LLMs and AI agents. Develop high-quality security datasets, including synthetic data, hard negatives, adversarial examples, expert annotations, and model-generated trajectories. Build reliable graders, verifiers, and reward signals using tests, compilation, runtime behavior, security-tool outputs, detection results, vulnerability reproduction, and other domain-specific validation methods. Design evaluations for security reasoning, code understanding, threat analysis, root-cause analysis, tool use, long-horizon execution, and autonomous task completion. Analyze model failures, data quality issues, grader weaknesses, reward hacking, benchmark overfitting, and capability regressions. Develop and evaluate post-training methods, including supervised fine-tuning, preference optimization, reinforcement learning, reward modeling, rejection sampling, and distillation. Build iterative model-improvement loops using model rollouts, verifier feedback, expert review, synthetic data generation, and failure-driven data collection. Conduct controlled experiments with appropriate baselines, ablations, and regression testing. Collaborate with Security Researchers, ML Engineers, infrastructure teams, and product teams to move research into production capabilities.

Your Experience Required Qualification: Strong hands-on experience in one or more cybersecurity areas, such as vulnerability research, secure coding, threat detection, malware analysis, incident investigation, reverse engineering, fuzzing, or security automation. Practical experience developing or evaluating