Manager, Software Engineering - Security Firmware

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

Manager for a team building security-critical root-of-trust (RoT) firmware for NVIDIA Data Center Systems. The role involves leading, mentoring, and growing a distributed team, driving modern software engineering practices, and championing an AI-forward engineering culture. The firmware establishes hardware-rooted security for boot integrity, cryptographic attestation, and secure lifecycle management.

What you'd actually do

  1. Own the delivery, quality, and security posture of root-of-trust firmware across NVIDIA’s data center compute platforms, from architecture through production release.
  2. Lead, mentor, and grow a distributed team of senior firmware and security engineers, fostering a culture of autonomy, accountability, and continuous learning.
  3. Drive adoption of modern software engineering practices: rigorous code review, robust CI/CD pipelines, automated testing and fuzzing for security-critical code paths, and systematic threat modeling.
  4. Champion an AI-forward engineering culture — actively using and encouraging AI coding assistants, automated analysis tools, and LLM-assisted workflows to improve team velocity and code quality.
  5. Establish and maintain effective asynchronous-first communication practices that enable a geographically distributed team to collaborate with clarity and minimal friction across time zones.

Skills

Required

  • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
  • 10+ overall years of relevant software or firmware engineering experience, including meaningful work in security-critical, embedded, or low-level systems software.
  • 5+ years of engineering management experience, with a track record of building and scaling high-performing teams.
  • Demonstrated experience managing distributed, remote-first engineering teams across multiple time zones, with a clear philosophy for enabling autonomous, high-agency contributors.
  • Deep familiarity with modern software engineering methodologies: agile/iterative development, continuous integration, test-driven development, and systematic code review practices.
  • Genuine, demonstrable AI-forward mindset: you actively use AI coding assistants and LLM-based tooling in your own workflows and have driven adoption of these tools within engineering teams.
  • Solid technical foundation in C/C++ and embedded systems, with the ability to engage credibly in deep technical discussions about firmware architecture, memory safety, and hardware-software interfaces.
  • Excellent written and verbal communication skills, with a strong preference for written communication that creates clarity and institutional memory for a remote team.
  • Comfortable with ambiguity and complexity; you make sound decisions quickly with incomplete information and course-correct without friction.

Nice to have

  • Hands-on experience with root-of-trust architectures, secure boot, hardware security modules (HSMs), cryptographic attestation, or similar security-critical firmware domains.
  • Experience with NIST SP 800-193 Platform Firmware Resiliency guidelines, DICE (Device Identifier Composition Engine), or SPDM (Security Protocol and Data Model).
  • Familiarity with NVIDIA Data Center platforms (DGX, HGX, MGX) or equivalent hyperscale infrastructure, including in-band and out-of-band management stacks.
  • Experience building or contributing to AI-assisted development tooling: prompt engineering for code generation, retrieval-augmented engineering workflows, or integrating LLMs into CI pipelines.
  • Proven track record to build and sustain a strong team culture across remote, asynchronous settings — including hiring, onboarding, career development, and performance management for distributed engineers.
  • Prior experience with formal threat modeling methodologies (STRIDE or similar) applied to firmware or embedded security contexts.

What the JD emphasized

  • security-critical
  • firmware
  • embedded
  • low-level systems software
  • AI-forward engineering culture
  • AI coding assistants
  • LLM-assisted workflows