Senior AI Security Researcher

🕒 July 8

🌲 North Carolina, Texas, +2 more states – Remote

🌲 North Carolina – Remote 🤠 Texas – Remote ⚔️ Virginia – Remote ☕ Washington – Remote

💵 $224k - $431.3k / year

⏰ Full Time

🟠 Senior

👮‍♂️ Cybersecurity / Security Engineer

🦅 H1B Visa Sponsor

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👻 Ghost score 16%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Develop and answer open-ended AI security research questions to help NVIDIA understand, measure, and reduce risk in frontier models, agentic systems, AI platforms, and AI-enabled products • Develop practical methods, prototypes, evaluations, or tools that reveal how AI systems can fail under adversarial conditions and how those risks can be mitigated • Explore AI security problems such as LLM and agent security, adversarial testing, model evaluation, cyber-defense automation, vulnerability discovery, secure deployment, and autonomous response • Translate research into usable outcomes for engineering and security teams, including proof-of-concept demonstrations, benchmarks, technical guidance, mitigations, and secure-by-design recommendations • Collaborate across offensive security, product security, AI research, platform, cloud, and infrastructure teams • Help shape NVIDIA's AI-security research strategy by mentoring others, identifying emerging risks, and building repeatable practices for evaluating and defending AI systems

🎯 Requirements

• 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 • Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Cybersecurity or a related field • 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 • Published work or public technical leadership in AI security, malware data science, adversarial ML, LLM security, cyber-defense automation, or offensive AI • Experience 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 • Experience with low-level systems security • History of mentoring researchers, winning or leading research programs, filing patents, publishing papers, or speaking at major security and AI venues

🏖️ Benefits

• Equity • Benefits