NVIDIA is powering the world's most advanced AI Factories. To ensure their seamless operation, we are building a mission-critical Observability and Prediction platform - delivered as both a high-scale SaaS solution and a robust on-premises deployment for our largest enterprise customers.
We are looking for a Senior Software Engineer to join the AIOps platform team and help build the core distributed systems that ingest massive telemetry streams from GPU clusters and operationalize predictive AI models at scale. You will work at the intersection of high-performance data engineering and production ML, turning research algorithms into reliable, mission-critical software.
What you'll be doing:
Architect and build an agentic AIOps system that autonomously monitors GPU fleet health, aggregates and correlates massive telemetry streams, surfaces intelligent alerts, and orchestrates multi-step diagnostic workflows and corrective actions - powering real-time dashboards, automated root-cause analysis, and proactive incident response.
Research, evaluate, and prototype data storage strategies and data representations across diverse database technologies and modalities, ensuring AI models are trained on high-quality, well-structured data that improves predictive accuracy and generalization.
High-Scale Engineering: Design distributed systems to handle the extreme telemetry density of large-scale AI clusters, ensuring efficient data ingestion, processing, and real-time analysis.
Instrument services with deep observability (metrics, logs, traces) to support rapid debugging and continuous performance improvement.
Build and own the model-serving infrastructure that operationalizes predictive algorithms at scale - packaging, versioning, deploying, and monitoring AI models in both SaaS and on-premises environments.
Contribute to the platform's core libraries and abstractions that accelerate development across the broader AIOps engineering team.
What we need to see:
B.Sc./M.Sc. in Computer Science, Computer Engineering, or a related technical field.
8+ years of software engineering experience building production distributed systems.
Core Systems Programming: Expert-level proficiency in languages such as Go, C++, or Rust, with a focus on high-performance, concurrent architectures.
Solid understanding of Kubernetes and container-based deployments for production services.
Experience deploying, monitoring, and maintaining ML models or data-intensive services in a production environment.
Comfort working in ambiguous, fast-moving environments where the product is still being shaped.
Ways to stand out from the crowd:
Experience building ML model-serving platforms or MLOps tooling (model registries, A/B rollout frameworks, feature stores) at scale.
A track record of taking systems from prototype to stable, production-grade platform serving real enterprise customers.
A "Systems" Thinker: You don't just write software; you understand the full stack, from how data moves across the wire to how it’s processed in a distributed cluster.
Practical Innovation: The ability to simplify complex problems and build internal tools or frameworks that empower other engineering teams to move faster.
With competitive salaries and a generous benefits package, 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 are passionate about building mission-critical systems at the frontier of AI infrastructure, we want to hear from you.
Skills Required
- B.Sc./M.Sc. in Computer Science, Computer Engineering, or related field
- 8+ years software engineering experience building production distributed systems
- Expert-level proficiency in Go, C++, or Rust with high-performance concurrent architectures
- Solid understanding of Kubernetes and container-based deployments
- Experience deploying, monitoring, and maintaining ML models or data-intensive services in production
- Comfort working in ambiguous, fast-moving environments where product requirements evolve
- Experience building ML model-serving platforms or MLOps tooling (model registries, A/B rollout frameworks, feature stores)
- Proven track record taking systems from prototype to production-grade platform serving enterprise customers
- Systems-level thinking and ability to build internal tools or frameworks to accelerate teams
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
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.”
