Nvidia Senior Site Reliability Engineer AI Infrastructure 

United States, Texas 

52266324

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.