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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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The Senior Platform AI Engineer joins the Silicon Co-Design Group to lead the end-to-end delivery of an efficiency platform supporting an intelligent automation ecosystem. This role involves defining architectural direction, infrastructure investments, and roadmap priorities across silicon architecture, build, methodology, validation, and applied AI teams. You will own the platform from design through production operation, managing orchestration patterns, authentication, observability, and storage strategies for heterogeneous compute environments. Key responsibilities include setting engineering standards, mentoring senior engineers, and resolving cross-team architectural challenges. Required skills include proficiency in Python and at least one compiled language like C, C++, Go, or Rust, alongside experience with Kubernetes Jobs, Celery, Sidekiq, or Temporal. The work focuses on building foundational ML infrastructure for silicon development, characterization, and debug workflows.
Define the architectural direction, infrastructure investments, and roadmap priorities for the efficiency platform.
Own the end-to-end delivery of the platform from initial design and implementation through sustained production operation.
Manage platform security, reliability, performance, and evolution across the intelligent automation ecosystem.
Lead unified solutions including orchestration patterns, authentication, authorization, observability, and SLA enforcement.
Manage storage and caching strategies that scale across heterogeneous compute environments.
Serve as the technical authority for AI-powered infrastructure by setting engineering standards and resolving architectural challenges.
Mentor senior engineers and grow technical talent within the organization.
What we're looking for
BS, MS, or PhD in CS, EE, CE, or a related field, or equivalent experience.
8+ years of hands-on experience designing and operating production-grade platform or backend infrastructure.
5+ years of direct ML infrastructure experience, including end-to-end ownership of a model serving platform or latency-sensitive backend service.
Demonstrated track record of setting technical direction, defining platform strategy, and establishing architectural standards at a department or company level.
Strong Python skills and proficiency in at least one compiled language such as C, C++, Go, Java, or Rust.
Hands-on experience with job queues and sandboxed execution, including Kubernetes Jobs, Celery/Sidekiq/Temporal, and container runtimes.
Strong communication and leadership skills to align senior team members and drive architectural decisions across organizations.
Industry recognition in ML infrastructure or distributed systems through publications, talks, or open-source contributions (preferred).