NVIDIA is powering the world's most advanced AI Factories. Keeping them running depends on an Observability and Prediction platform that continuously ingests telemetry from every GPU, NIC, switch and link in the fleet - delivered both as a high-scale SaaS service and as a self-contained on-premises deployment for our largest enterprise customers.
We are looking for a Senior Software Engineer to build the backend platform underneath it: the distributed services that move, transform, store and serve telemetry at extreme volume, and the infrastructure that makes them deployable, upgradable and survivable on clusters ranging from a single node to tens of thousands of GPUs. This is the substrate every analytics, alerting and AI layer runs on - if it isn't fast, correct and resilient, nothing above it works.
What you'll be doing:
Build the high-scale data path. Design and implement the services that ingest, normalize, enrich and route enormous, continuous telemetry streams. You'll own throughput, back-pressure, concurrency, and how the system scales horizontally as clusters grow by an order of magnitude.
Design the storage and query layer. Choose the right data model and store for each kind of data, and make queries over very large datasets return fast enough to drive live dashboards, alerting and automated analysis. Own the trade-offs - cardinality, retention, cost, latency - and the APIs the rest of the product is built on.
Make it resilient. Design for partial failure: what happens when a node disappears, when state is lost, when a service restarts into the wrong configuration. Eliminate silent-failure modes where a service looks healthy while delivering nothing.
Own how the platform ships and runs. Packaging, configuration, upgrade paths, backup and restore, and clean deployment onto customer-controlled environments - so that software you can't log into still behaves correctly on day 400.
Push on performance and capacity. Profile the system under realistic load, find the real bottleneck rather than the suspected one, fix it, and translate the results into concrete sizing and scaling guidance.
What we need to see:
B.Sc./M.Sc. in Computer Science, Computer Engineering, or a related technical field.
5+ years of software engineering experience building production backend systems.
Strong proficiency in languages as Go, C++, Rust or Python, with solid fundamentals in concurrency, memory and performance - and the flexibility to work across languages in a polyglot codebase.
Hands-on experience with distributed systems: services that talk to each other over a network, fail independently, and have to stay correct anyway.
Practical experience with at least one class of data infrastructure - streaming/messaging systems, databases (relational, time-series, analytical, graph or key-value), or high-throughput data pipelines. We care that you understand the trade-offs, not that you've used our specific stack.
Ways to stand out from the crowd:
Experience shipping software that other organizations install and operate themselves: versioning, compatibility, upgrades, and debugging environments you don't control.
Depth in large-scale data systems - you've dealt with the point where the obvious design stops working and had to redesign it.
Networking or datacenter-infrastructure background, or experience with telemetry, monitoring and observability systems.
A systems thinker: you understand the full stack, from how data moves across the wire to how it's processed in a distributed cluster, and you can tell us where the time went.
Skills Required
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related technical field
- 5+ years of software engineering experience building production backend systems
- Strong proficiency in Go, C++, Rust, or Python
- Strong fundamentals in concurrency, memory management, and performance
- Hands-on experience with distributed systems
- Practical experience with streaming or messaging systems, databases, or high-throughput data pipelines
- Experience shipping self-installable and self-operated software, including versioning, compatibility, upgrades, and debugging
- Experience with large-scale data systems
- Networking or datacenter infrastructure experience, or experience with telemetry, monitoring, and observability systems
- Full-stack systems understanding across networking, data processing, and distributed clusters
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.
-
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.
-
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.
-
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.”
