Senior Site Reliability Engineer, AIOPs

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 148,000 - 276,000; Full time

Job summary

NVIDIA Gruppe in Santa Clara is seeking an experienced engineer to build an AI Data Center AIOps platform. The ideal candidate will have a strong background in Kubernetes and automation, ensuring the reliability of GPU fleet management.

Key responsibilities include monitoring platform health, owning infrastructure deployments, and leading incident resolution. Candidates should possess 5+ years of experience in production systems and a degree in CS/CE. A competitive salary and generous benefits package are offered.

Qualifications

• 5+ years operating production distributed systems as SRE/DevOps/Platform Ops.
• Proven ownership of reliability for an observability/AIOps platform.
• Deep Kubernetes and containers experience for telemetry-heavy microservices.

Responsibilities

• Continuously monitor platform health via dashboards, logs, and metrics.
• Own Kubernetes deployments end-to-end including runbooks and validation.
• Lead first-level incident triage and collect diagnostics.

Skills

Kubernetes

Python

Bash

Distributed systems

Automation

Education

BS/MS in CS/CE or equivalent experience

Tools

Terraform

Helm

Prometheus

Grafana

Job description

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 NVIDIA, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.

Role Overview

You will be building an AI Data Center AIOps platform that turns raw, high‑volume telemetry into reliable, job‑centric insights and automation for GPU fleets. Join our team of innovative engineers who are building this platform and operating it (not the compute cluster): uptime, performance, data integrity, and safe change management. You’ll own SLOs/SLIs, incident response, and postmortems for the telemetry ingestion, processing, storage, and APIs/dashboards that operators depend on. You’ll partner with the Software Engineering and Systems Engineering team to translate platform signals into actionable, trustworthy alerts and automation.

Responsibilities

• Continuously monitor platform health via dashboards, logs, and metrics, automate recurring checks, and keep reliability and resource efficiency on track.
• Own Kubernetes deployments end‑to‑end (runbooks, canary checks, post‑deploy validation), and lead rollbacks/remediations when needed.
• Lead first‑level incident triage: collect diagnostics, identify likely root causes, and hand off clear, actionable findings to engineering.
• Build and maintain runbooks, SOPs, and checklists, pushing continuous improvement through automation.
• Manage deployment infrastructure and packaging (Helm + Terraform/IaC) to keep environments scalable, consistent, and reproducible.
• Contribute in adjacent functional areas to grow and help your team members.

Qualifications

• BS/MS in CS/CE (or equivalent experience) and 5+ years operating production distributed systems as SRE/DevOps/Platform Ops.
• Proven ownership of reliability for an observability/AIOps platform: SLOs/SLIs, on‑call, addressing incidents, and follow‑up evaluations that drive measurable improvements.
• Deep Kubernetes and containers experience (deploying, debugging, scaling) for telemetry‑heavy microservices—ingestion, processing, storage, APIs, and UI.
• Automation‑first approach: solid scripting (Python/Bash), CI/CD, and infrastructure‑as‑code (Terraform + Helm) to deliver safe rollouts (canaries/rollbacks), reproducible environments, and minimal toil.
• Clear communicator who writes excellent runbooks/docs and can translate ambiguous requirements into concrete operational practices and dependable customer‑facing reliability.

Ways to Stand Out

• Strong Linux and networking fundamentals, distributed systems instincts, and hands‑on ops for Kubernetes/services/streaming stacks are ideal; bonus for experience with observability platforms at scale.
• Experience building safe automation that operators trust: canary releases, automated rollback criteria, “monitoring for the monitoring” (lag/drop/error budgets), and replay/backfill pipelines with correctness checks.
• Strong in distributed/streaming systems operations (Kafka/Pulsar, Flink/Spark, ClickHouse/Elastic/TSDBs, object storage)—and can reason about backpressure, hotspots, and failure domains end‑to‑end.
• Proven programming experience building automation tools or services—ideally in Python, or similar languages—to simplify operations and scale recurring processes.
• Proven experience running large‑scale production deployments and multiple Kubernetes environments or clusters across teams or customers, coordinating changes and rollouts with minimal disruption with hands‑on experience with observability tools—you know your way around dashboards, metrics, logs, and traces using platforms like Prometheus, Grafana, or similar.

Benefits

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000USD – 235,750USD for Level3, and 176,000USD – 276,000USD for Level4. You will also be eligible for equity and benefits. NVIDIA offers a competitive salaries and generous benefits package.

EEO Statement

NVIDIA is committed to fostering a diverse work environment and prides itself as an equal opportunity employer. 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.