Senior AI Security Researcher

NVIDIA NVIDIA · Semiconductors · Durham, NC +5 · Remote

Senior AI Security Researcher at NVIDIA focusing on testing, attacking, defending, and safely deploying frontier AI systems, agentic applications, and AI-enabled security automation. The role involves developing new methods, tools, and evaluations to understand and mitigate security risks across various AI components.

What you'd actually do

  1. Develop and answer open-ended AI security research questions that helps NVIDIA understand, measure, and reduce risk in frontier models, agentic systems, AI platforms, and AI-enabled products.
  2. Develop practical methods, prototypes, evaluations, or tools that reveal how AI systems can fail under adversarial conditions and how those risks can be mitigated.
  3. Explore a range of AI security problems, such as LLM and agent security, adversarial testing, model evaluation, cyber-defense automation, vulnerability discovery, secure deployment, or autonomous response.
  4. Translate research into usable outcomes for engineering and security teams, including proof-of-concept demonstrations, benchmarks, technical guidance, mitigations, and secure-by-design recommendations.
  5. Collaborate across offensive security, product security, AI research, platform, cloud, and infrastructure teams to connect research insights with NVIDIA's highest-impact security priorities.

Skills

Required

  • AI security
  • Cybersecurity research
  • Applied ML research
  • Offensive security
  • Cyber defense
  • Python
  • PyTorch
  • JAX
  • TensorFlow
  • scikit-learn
  • Pandas
  • NumPy
  • Spark
  • BigQuery
  • LLM security
  • Adversarial ML
  • Model evaluation
  • Agent security
  • Prompt injection
  • Model backdoors
  • Data poisoning
  • Model abuse
  • Secure RAG
  • Synthetic data
  • AI-enabled security automation
  • Threat modeling
  • Adversary simulation
  • Exploit research
  • Vulnerability research
  • Malware analysis
  • Network defense
  • Threat hunting
  • Detection engineering
  • Digital forensics
  • Secure code review
  • Incident-response automation

Nice to have

  • Published work or public technical leadership in AI security, malware data science, adversarial ML, LLM security, cyber-defense automation, or offensive AI.
  • Developing benchmarks, challenge datasets, red-team tools, evaluation suites, or simulation environments for AI and security systems.
  • Deep knowledge of attacker tradecraft, including living-off-the-land techniques, supply-chain abuse, application-layer AI attacks, data exfiltration, and abuse of autonomous tooling.
  • Low-level systems security.
  • Mentoring researchers
  • Filing patents
  • Publishing papers
  • Speaking at major security and AI venues.

What the JD emphasized

  • 12+ years of experience in AI security, cybersecurity research, applied ML research, offensive security, cyber defense, or related technical fields.
  • Demonstrated record of original research and practical impact, such as deployed security ML systems, AI-security evaluations, CVEs, patents, publications, conference talks, open-source tools, production mitigations, or funded research programs.
  • Hands-on ability to build working research systems in Python and modern ML/data tooling such as PyTorch, JAX, TensorFlow, scikit-learn, Pandas, NumPy, Spark, BigQuery, or comparable platforms.
  • Experience with one or more AI-security areas: LLM security, adversarial ML, model evaluation, agent security, prompt injection, model backdoors, data poisoning, model abuse, secure RAG, synthetic data, or AI-enabled security automation.
  • Strong cybersecurity foundation, including threat modeling, adversary simulation, exploit or vulnerability research, malware analysis, network defense, threat hunting, detection engineering, digital forensics, secure code review, or incident-response automation.
  • Ability to work across ambiguous research problems and practical product constraints, translating findings into prioritized recommendations and measurable security outcomes.
  • Experience leading AI-security research for major models, AI platforms, security products, or large-scale production systems.
  • A track record of building security ML systems that operate at real-world scale

Other signals

  • AI security research
  • evaluating and defending AI systems
  • adversarial ML
  • LLM security