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

Palo Alto Networks • Santa Clara, CA

65d • $163,200 - $264,000 • Onsite

About The Position

As a Sr 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.

Requirements

• 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 LLM-based systems for cybersecurity, source code, software engineering, or tool-using agents.
• Experience designing datasets, benchmarks, graders, evaluation frameworks, or tasks with verifiable outcomes.
• Experience with at least one post-training or model-adaptation method, such as supervised fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data generation, or distillation.
• Strong programming skills in Python and at least one system or application language, such as C, C++, Rust, Java, or Go.
• Experience working with large codebases, security tools, testing frameworks, build systems, debuggers, and containerized environments.
• Strong understanding of experimental design, failure analysis, regression testing, and quantitative evaluation.
• Ability to independently drive ambiguous research problems from investigation through implementation and validation.
• BS/MS degree in Computer Science, Machine Learning, Artificial Intelligence, Cybersecurity, or a related field, or equivalent practical experience.

Nice To Haves

• Experience building reinforcement-learning environments, cybersecurity agents, coding agents, or other tool-using AI systems.
• Experience with DPO, RLHF, RLAIF, online or offline reinforcement learning, process supervision, or model distillation.
• Experience developing verifiable cybersecurity tasks using tests, sanitizers, fuzzing, symbolic execution, malware sandboxes, detection systems, incident data, exploit reproduction, or patch validation.
• Experience with vulnerability research automation, AI-assisted security analysis, threat detection, malware analysis, incident response, static or dynamic analysis, or software supply-chain security.
• Experience training or evaluating code-focused or cybersecurity-focused language models and autonomous agents.
• Experience with distributed training, large-scale inference, rollout generation, or production ML platforms.
• Publications, open-source contributions, patents, vulnerability disclosures, or other demonstrated research impact in cybersecurity, LLM post-training, reinforcement learning, code intelligence, or AI agents.

Responsibilities

• 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.

Benefits

• The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Associate degree