Senior Platform AI Engineer

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

Senior AI Platform Engineer at NVIDIA responsible for defining the architectural direction, infrastructure investments, and roadmap priorities for an efficiency platform supporting an intelligent automation ecosystem. This role involves end-to-end delivery of the platform, including orchestration, authentication, observability, and SLA enforcement, with a focus on ML infrastructure and large-scale systems.

What you'd actually do

  1. Define the architectural direction, infrastructure investments, and roadmap priorities for the efficiency platform — across silicon architecture, build, methodology, validation, and applied AI teams.
  2. Define platform contracts and onboard new agents and skills from the domain teams across SCG building on the platform.
  3. Owning end-to-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution.
  4. Leading the unified solutions including orchestration patterns, authentication and authorization, observability, and SLA enforcement.
  5. Serving as the technical authority for infrastructure powered by artificial intelligence across SCG: setting engineering standards, resolving cross-team architectural challenges, and mentoring senior engineers.

Skills

Required

  • BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field
  • 8+ years of hands-on experience designing and operating production-grade platform or backend infrastructure
  • 5+ years of direct ML infrastructure experience
  • end-to-end ownership of a model serving platform or latency-sensitive backend service
  • Demonstrated track record of setting technical direction at the department or company level
  • Strong Python skills
  • proficiency in at least one compiled language such as C, C++, Go, Java, or Rust
  • Hands-on experience with job queues + sandboxed execution (Kubernetes Jobs, Celery/Sidekiq/Temporal, container runtimes with resource isolation)
  • Strong communication and leadership skills

Nice to have

  • Industry recognition in ML infrastructure or distributed systems — through publications, conference talks, open-source contributions, or technical leadership visible beyond your current organization
  • Exposure to silicon design, methodology, validation or EDA toolchains, especially the cadence of chip development lifecycles
  • Experience building or operating AI platforms within a silicon development, validation or EDA environment, with a firsthand understanding of the reliability and scale demands of chip design toolchains
  • Track record of mentoring senior engineers and growing technical talent

What the JD emphasized

  • end-to-end ownership of a model serving platform or latency-sensitive backend service from initial architecture through sustained production operation
  • setting technical direction at the department or company level: defining platform strategy, establishing architectural standards, and leading initiatives spanning multiple teams
  • Experience driving platform architecture at company scale, including engineering standards or frameworks broadly adopted by other teams
  • Track record of mentoring senior engineers and growing technical talent — shaping the capabilities of the team as much as the platform itself

Other signals

  • ML infrastructure
  • large-scale systems
  • production operation
  • AI platforms
  • silicon architecture