NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. 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 best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA's Silicon Co-Design Group (SCG) is seeking Senior AI Platform Engineers to set the technical direction and own end-to-end delivery of the AI-driven efficiency platform powering our intelligent automation ecosystem. Every CPU, GPU, and Tegra SoC NVIDIA has shipped in the past four years passed through our toolchain on its way to production — over 200 product SKUs were optimized during the Blackwell generation alone. Now we're rebuilding that toolchain around AI, and we need the engineer who defines the architecture everyone else builds on. In this role, you will set the platform strategy, own the full lifecycle of a production AI infrastructure, and drive alignment across multiple engineering departments — with personal accountability for its reliability, scalability, and long-term evolution. If you are energized by building foundational platforms at the intersection of ML infrastructure and large-scale systems, this is your opportunity. At NVIDIA, we strive for perfection, encourage innovation, and provide opportunities to explore new ways to succeed!

What you'll be doing:

Leading technical strategy and roadmap for the AI-driven efficiency platform to meet SCG cross-functional use cases, investment areas and priority: defining architectural direction, making infrastructure investment decisions, and aligning roadmap priorities across silicon design, methodology, validation, and applied AI teams.
Partnering with domain agent builders across SCG teams and functional domains to define platform contracts and onboard new agents and skills.
Owning end-to-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution.
Driving platform-wide decisions with cross-functional impact and leading the unified solutions: orchestration patterns, authentication and authorization, observability and SLA enforcement, and storage and caching strategies that scale across heterogeneous compute environments.
Serving as the technical authority for AI-driven infrastructure across SCG: setting engineering standards, resolving cross-team architectural conflicts, and mentoring senior engineers.
Identifying gaps and opportunities at the frontier of AI-driven infrastructure tooling — evaluating emerging technologies, shaping internal standards, and contributing learnings back to SCG engineering organization.

What we need to see:

BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, with 12+ 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).
Proven ability to own high-stakes systems with rigorous operational discipline: structured observability, graceful degradation, clearly defined SLOs, and a sustained track record of reliability under pressure.
Strong communication and leadership skills, with the ability to align senior stakeholders and drive architectural decisions across organizations with competing 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 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.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family (www.nvidiabenefits.com).

What you'll be doing:

Leading technical strategy and roadmap for the AI-driven efficiency platform to meet SCG cross-functional use cases, investment areas and priority: defining architectural direction, making infrastructure investment decisions, and aligning roadmap priorities across silicon design, methodology, validation, and applied AI teams.

Partnering with domain agent builders across SCG teams and functional domains to define platform contracts and onboard new agents and skills.

Owning end-to-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution.

Driving platform-wide decisions with cross-functional impact and leading the unified solutions: orchestration patterns, authentication and authorization, observability and SLA enforcement, and storage and caching strategies that scale across heterogeneous compute environments.

Serving as the technical authority for AI-driven infrastructure across SCG: setting engineering standards, resolving cross-team architectural conflicts, and mentoring senior engineers.

Identifying gaps and opportunities at the frontier of AI-driven infrastructure tooling — evaluating emerging technologies, shaping internal standards, and contributing learnings back to SCG engineering organization.

What we need to see:

BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, with 12+ 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).

Proven ability to own high-stakes systems with rigorous operational discipline: structured observability, graceful degradation, clearly defined SLOs, and a sustained track record of reliability under pressure.

Strong communication and leadership skills, with the ability to align senior stakeholders and drive architectural decisions across organizations with competing 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 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.