NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for over 30 years. It’s a unique legacy of innovation that’s fueled by phenomenal technology and outstanding 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, encouraging environment where everyone is inspired to do their best work. Come join the AI Infrastructure Production engineering team and see how you can make a lasting impact on the world.
What You Will Be Doing:
Develop and maintain large-scale systems supporting critical use cases for AI Infrastructure, driving reliability, operability, and scalability across global public and private clouds.
Implement SRE fundamentals, including incident management, monitoring, and performance optimization, while designing automation tools to reduce manual processes and operational overhead.
Build tools and frameworks to improve observability, define actionable reliability metrics, and enable fast issue resolution, driving continuous improvement in system performance.
Establish frameworks for operational maturity, lead sustainable incident response protocols, and conduct blameless postmortems to improve team efficiency and system resilience.
Work with engineering teams to deliver innovative solutions, mentor peers, uphold high standards for code and infrastructure, and contribute to hiring for a diverse, high-performing team.
What We Need to See:
Degree in Computer Science or related field, or equivalent experience with 12+ years in Software Development, SRE, or Production Engineering.
Proficiency in Python and at least one other language (C/C++, Go, Perl, Ruby).
Expertise in systems engineering within Linux or Windows environments and cloud platforms (AWS, OCI, Azure, GCP).
Strong understanding of SRE principles, including error budgets, SLOs, SLAs, and Infrastructure as Code tools (e.g., Terraform CDK).
Hands-on experience with observability platforms (e.g., ELK, Prometheus, Loki) and CI/CD systems (e.g., GitLab).
Strong communication skills with the ability to convey technical concepts effectively to diverse audiences.
Commitment to fostering a culture of diversity, curiosity, and continuous improvement.
Ways to stand out from the crowd:
Experience in AI training, inferencing, and data infrastructure services.
Proficiency in deep learning frameworks like PyTorch, TensorFlow, JAX, and Ray.
A strong background in hardware health monitoring and system reliability.
Hands-on expertise in operating and scaling distributed systems with stringent SLAs, ensuring high availability and performance.
Proven experience in incident, change, and problem management processes, fostering continuous improvement in sophisticated environments.
Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
