NVIDIA’s System Software team builds foundational software that enables deterministic, high-performance computing platforms by shifting complexity from silicon into software. We design and maintain the hardware abstraction layers, core system libraries, and runtime components that allow compiler teams and data center operators to safely and efficiently execute workloads on novel architectures. In this role, you will develop and evolve the libraries, drivers, and runtime interfaces that serve as key entry points into the platform. You will also help improve reliability and operability through automation, diagnostics, and tight cross-org collaboration with hardware, compiler, and operations teams.
What you'll be doing:
Extend and maintain hardware abstraction layers and core system libraries used across the platform.
Design and implement drivers, runtimes, and data movement/aggregation pipelines supporting workload execution.
Build and maintain runtime interfaces for launching, monitoring, and managing workloads.
Improve platform reliability through automation, error reporting, diagnostics, and operational tooling.
Debug and resolve complex sequencing, initialization, and runtime issues across multi-component systems.
Partner cross-functionally with hardware engineering, compiler teams, and data center operations to bring features from prototype to production.
Support new platform bring-up and NPI (New Product Introduction) efforts for new boards and silicon.
Contribute to engineering excellence through documentation, tooling improvements, code reviews, and knowledge sharing.
What we need to see:
A Masters Degree in Computer Science, Computer Engineering, Electrical Engineering, related STEM field or equivalent experience.
5+ years of relevant work experience
Strong proficiency in modern C++ (design, implementation, debugging, and performance considerations).
Experience designing, maintaining, and refactoring software libraries and APIs with long-term support in mind.
Comfort working in large, multi-repository or multi-component codebases with layered dependencies.
Demonstrated ability to lead or drive triage of difficult reliability issues and produce clear root-cause analysis.
Ability to clearly communicate software architecture and design tradeoffs, including using diagrams and written design docs.
Low-level platform software experience (e.g., firmware/boot flows, RTOS, BMCs/MCUs, RISC-V, or closely related system software).
Linux systems experience that includes driver or kernel-adjacent interfaces (e.g., VFIO or similar subsystems).
Hardware bring-up and/or system triage experience (fault analysis, system diagnostics, or validation support in lab environments).
Ways to stand out from the crowd:
Distributed systems experience (e.g., MPI, gRPC, RPC frameworks, coordination/telemetry patterns).
Experience with inference systems and token serving (e.g., vLLM or similar serving/runtime stacks).
Experience shipping and supporting customer-facing SDKs, including documentation and ABI compatibility practices.
Production readiness and delivery experience (e.g., CI/CD and release workflows, monitoring/alerting practices, Kubernetes and/or data center operational workflows).
The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Today, NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.”
Widely considered to be one of the technology world’s most desirable employers, NVIDIA has some of the most forward-thinking and hardworking people in the world inventing the future with us. Are you a creative and collaborative software engineer seeking new challenges? If so, we want to hear from you! Come, join us and help build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
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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.”
