Principal AI Model Security Researcher

Remote work within the United States will be considered for highly qualified candidates. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Doctorate in relevant field AND 3+ years related research experience OR Master's Degree in relevant field AND 4+ years related research experience OR Bachelor's Degree in relevant field AND 6+ years related research experience OR equivalent experience. These requirements include, but are not limited to the following specialized security screenings: Hands-on expertise in adversarial machine learning, AI security, model evaluation, privacy attacks, offensive security or a closely related discipline. Experience designing experiments or security investigations that produce reproducible technical evidence and actionable remediation. Programming and research-prototyping ability sufficient to build attack tooling, evaluation harnesses and automated tests. Experience testing large language models, post-training or reinforcement-learning systems, agentic systems or AI-enabled security products. Knowledge of reward modeling, interpretability, capability elicitation, data extraction, poisoning, privacy or model supply-chain security. Experience with cyber exploitation, cloud or identity security, sandboxing, secure evaluation environments or offensive-security operations. Demonstrated ability to communicate complex attack paths, severity and residual risk to researchers, engineers and senior decision-makers. Experience converting research findings into production evaluations, mitigations, training interventions or security controls. Design and execute white-box attacks against advanced AI models, checkpoints, post-training systems, reward functions, data pipelines and evaluation environments. Investigate adversarial behavior including jailbreaks, harmful permissiveness, reward hacking, deceptive behavior, capability elicitation, privacy leakage, data poisoning and model supply-chain compromise. Build high-fidelity experiments and automated attack suites that reproduce realistic threats and run continuously against new checkpoints and system configurations. Develop rigorous threat models, attack hypotheses and reproducible evidence; assess severity, exploitability and customer or governance impact. Partner with model scientists, engineers and product security teams to identify root causes and drive findings to verified remediation. Convert confirmed weaknesses into regression evaluations, adversarial training or RL environments, safety training data and stronger technical controls. Communicate findings and residual risk clearly to technical teams, leadership and independent reviewers.