AI-Driven GPU Software Security Engineer

NVIDIA Corporation; Santa Clara (CA); On-site; USD 152,000 - 242,000; Full time

Job summary

NVIDIA is seeking a highly motivated security engineer to join the GPU Software team in Santa Clara, CA. You will focus on automating security checks across every software release, using both traditional tooling and AI agents, and help teams across the organization adopt them.

You will own and maintain the release security scanning pipeline, collaborate in threat modeling and remediation, and drive adoption of agentic workflows across engineering teams.

Qualifications

• 5+ years in security engineering, with experience in drivers, kernel, or fw.
• Strong programming skills in Python and C.
• Hands‑on experience building LLM agents or agentic workflows.
• Experience integrating tooling into CI/CD and release pipelines.
• Track record of taking an internal tool from prototype to broad adoption, without direct authority.
• BS or MS degree in Computer Engineering, Computer Science, or related field.

Responsibilities

• Lead our adoption of AI agents in security, finding manual workflows and reinventing them.
• Take proven agentic workflows through development and into production, drive adoption across engineering teams.
• Translate security requirements into automated, per-release checks across firmware to user-mode apps.
• Own and maintain the release security scanning pipeline, and prioritize its findings with product teams.
• Collaborate in threat modeling, architecture reviews, vulnerability mitigation & remediation.

Skills

Security engineering

Python

C

LLM agents

CI/CD integration

Education

BS or MS in Computer Engineering/CS

Job description

NVIDIA is seeking a highly motivated security engineer to join the GPU Software team in Santa Clara, CA. You will focus on automating security checks across every software release, using both traditional tooling and AI agents, and help teams across the organization adopt them.

You will own and maintain the release security scanning pipeline, collaborate in threat modeling and remediation, and drive adoption of agentic workflows across engineering teams.