Lead deployment, administration, and strategic use of the HiddenLayer platform to protect AI/ML systems from adversarial attacks and other AI-specific threats.
Responsibilities
Deploy, configure, and administer the HiddenLayer platform
Integrate HiddenLayer with enterprise SIEM, SOAR, EDR, vulnerability management, and cloud security platforms
Create detection rules, dashboards, and executive reporting on AI security posture
Protect AI models against adversarial attacks, model theft, prompt injection, model poisoning, and unauthorized inference
Develop monitoring and detection strategies for production AI workloads
Provide senior technical advisory and systems engineering expertise in AI/ML security and cybersecurity
Evaluate vulnerabilities and security risks involving AI systems, machine learning models, and supporting infrastructure
Assess adversarial machine learning threats, including model poisoning, model inversion, evasion techniques, adversarial attacks, and data integrity threats
Support AI red teaming, adversarial robustness testing, AI penetration testing, and related security evaluations
Assess secure data architectures and pipelines supporting large scale analytics and intelligence environments
Requirements
Bachelor's degree in physics, mathematics, computer science, cybersecurity, or a relevant engineering discipline, including electrical, computer, systems, or software engineering
Nice to have: Master's degree or PhD in a relevant technical discipline
Nice to have: AI security SME or technical advisor to a federal civilian agency
Design, prototype, and ship safety for AI driven threats across CBRN, Cyber, Agentic, Fraud risks with classifiers, runtime policy enforcement, and ML systems.
Own the technical vision and reference architecture for safeguarding agentic systems across tool use, planning, memory, and multi-agent interactions.
Lead threat modeling for increasingly complex attack surfaces, anticipating adversary behavior and translating it into concrete engineering requirements and defenses.
Shape the shared safety and security infrastructure that AI workloads depend on, ensuring defenses are reusable across products.
Work objectively across teams and team members to diagnose problems, recommend solutions, align on standards, and raise the engineering bar through design reviews, mentorship.
Requirements
Bachelor’s degree in Computer Science or equivalent practical experience
Nice to have: command of threat modeling
Nice to have: securing agentic or autonomous AI systems
Nice to have: agent platform technologies such as Vertex AI or Gemini Enterprise
Design and execute white-box attacks against advanced AI models, checkpoints, post-training systems, reward functions, data pipelines and evaluation environments.
Locations (2)
Redmond, Washington, United States
United States
Compensation
Research Sciences IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
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.
Senior Security Program Manager to establish and operate the program backbone for a new red team spanning adversarial machine learning, agent security, offensive security, automation and privacy.
Compensation
Technical Program Management IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
Design and operate the intake, prioritization and engagement-planning process for embedded assignments, release-focused pre-gate reviews and ad hoc requests.
Maintain the findings lifecycle from initial report through severity assessment, ownership, remediation target, adversarial validation, closure and conversion into durable regression coverage.
Coordinate red-team engagements across model, evaluation, engineering and product milestones, identifying dependencies and schedule risks early.
Build clear pre-gate packages containing threat models, attack coverage, findings, fixes, residual risk and supporting evidence for independent review.
Establish program metrics and operating reviews covering engagement coverage, escape rate, time to verified fix, finding durability and review efficiency.
Conduct hands-on red teaming of advanced AI models, design attacks to expose exploitable behavior, and turn critical findings into durable security improvements.
Locations (3)
Redmond, Washington, United States
San Francisco Bay Area, California, United States
New York City, New York, United States
Compensation
The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
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.
Founding technical leader to build and guide an AI red team with expertise across adversarial machine learning, agent security, offensive security, automation and privacy.
Compensation
The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.
Responsibilities
Build and develop a team of specialists spanning adversarial machine learning, agent security, offensive security, attack automation and privacy.
Set the attack strategy and severity bar for uncovering critical weaknesses in advanced AI models, agentic systems and AI-enabled products.
Lead hands-on red-team operations across models, training and evaluation systems, agent frameworks, tools, identities and permissions to discover exploitable behavior before release.
Design and operationalize high-fidelity attack campaigns, continuous adversarial testing and automated suites that reproduce realistic threats at scale.
Drive critical findings to verified remediation with research, engineering and product security teams, converting recurring weaknesses into regression evaluations, adversarial environments and stronger controls.
Senior AI Security Analyst for a one‑year term assignment at Loyola Marymount University, focusing on AI governance, cybersecurity, and cloud security.
Compensation
$89,100.00 - $120,400.00 Salary commensurate with education and experience.
Responsibilities
Leverage AI for SOC automation and alert tuning with Microsoft tools
Implement AI-powered threat detection and security monitoring across the full technology environment
Participate in security assessments, vulnerability testing, and threat simulations
Utilize AI-enabled security tools to identify potential vulnerabilities
Assist in evaluating AI-enabled applications, APIs, and related technologies for security risks
Requirements
Bachelor's degree in computer science, information systems management, business administration, or related field
Setting the strategic direction for AI-augmented delivery or the AI-era operating model across the enterprise, serving as a senior Frontier authority partnering with C-suite clients to co-design AI transformation strategies.
Responsibilities
Set the strategic direction for AI-augmented delivery or the AI-era operating model across the enterprise.
Co-design AI transformation strategies directly with C-suite client stakeholders and produce enterprise transformation blueprints.
Define evaluation frameworks for frontier-model deployment, including red-team protocols, safety benchmarks, and responsible-AI standards.
Govern adversarial-AI risk and AI safety policy, and lead incident-response strategy at the enterprise perimeter.
Own the direction of a Frontier practice and help structure outcome-based commercial models.
Lead enterprise AI strategy and governance, partnering with C-suite clients to design AI transformation strategies and govern frontier-model deployment.
Responsibilities
Set strategic direction for AI-augmented delivery or AI-era operating model across the enterprise
Co-design AI transformation strategies with C-suite client stakeholders
Define evaluation frameworks for frontier-model deployment, including red-team protocols and responsible-AI standards
Govern adversarial-AI risk and AI safety policy, and lead incident-response strategy at the enterprise perimeter
Own the direction of a Frontier practice and help structure outcome-based commercial models
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.
Principal Engineer, SafeguardsGoogle · Sunnyvale, US
AVP- Principal ArchitectCognizant · India