Job description

For over 25 years, NVIDIA has been revolutionizing computer graphics, PC gaming, and accelerated computing. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s be,a ,,ast talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join the team and discover how you can build a lasting impact on the world. NVIDIA's Silicon Co-Design Group (SCG) is seeking Senior AI Platform Engineers. They will set the technical direction and lead the end-to-end delivery of the efficiency platform that supports our intelligent automation ecosystem.. If you are energized by building foundational platforms at the intersection of ML infrastructure and large-scale systems, this is your opportunity! What you'll be doing: • Define the architectural direction, infrastructure investments, and roadmap priorities for the efficiency platform — across silicon architecture, build, methodology, validation, and applied AI teams. • Define platform contracts and onboard new agents and skills from the domain teams across SCG building on the platform. • Owning end-to-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution. • Leading the unified solutions including orchestration patterns, authentication and authorization, observability, and SLA enforcement. Driving platform-wide decisions with multi-functional impact. Managing storage and caching strategies that scale across heterogeneous compute environments. • 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. What we need to see: • BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, with 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 from initial architecture through sustained production operation. • Demonstrated track record of setting technical direction at the department or company level: defining platform strategy, establishing architectural standards, and leading initiatives spanning multiple teams. • 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 + sandboxed execution (Kubernetes Jobs, Celery/Sidekiq/Temporal, container runtimes with resource isolation). • Strong communication and leadership skills, with the ability to align senior team members and drive architectural decisions across organizations with contending priorities. Ways to stand out from the crowd: • Industry recognition in ML infrastructure or distributed systems — through publications, conference talks, open-source contributions, or technical leadership visible beyond your current organization. • Experience driving platform architecture at company scale, including engineering standards or frameworks broadly adopted by other teams. • 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 — shaping the capabilities of the team as much as the platform itself. The platform you architect will run every workflow involving NVIDIA silicon — from bring-up, characterization, and debug to production sign-off. If leading the foundation that the next generation of SCG engineers builds on is the problem you want to solve, we'd like to talk! Known throughout the technology industry as a highly attractive employer, NVIDIA delivers highly competitive salaries and a comprehensive benefits package. As you consider your future, explore what we can provide for you and your family at www.nvidiabenefits.com/ #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 29, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Define the architectural direction, infrastructure investments, and roadmap priorities for the efficiency platform — across silicon architecture, build, methodology, validation, and applied AI teams.
Define platform contracts and onboard new agents and skills from the domain teams across SCG building on the platform.
Owning end-to-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution.
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.
BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, with 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 from initial architecture through sustained production operation.
Demonstrated track record of setting technical direction at the department or company level: defining platform strategy, establishing architectural standards, and leading initiatives spanning multiple teams.
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 + sandboxed execution (Kubernetes Jobs, Celery/Sidekiq/Temporal, container runtimes with resource isolation).
Strong communication and leadership skills, with the ability to align senior team members and drive architectural decisions across organizations with contending priorities.
Industry recognition in ML infrastructure or distributed systems — through publications, conference talks, open-source contributions, or technical leadership visible beyond your current organization.
Experience driving platform architecture at company scale, including engineering standards or frameworks broadly adopted by other teams.
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 — shaping the capabilities of the team as much as the platform itself.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 29, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythongojavarustkubernetes

Platform: eightfold
Effort: medium

Benefits

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