We are now looking for a Principal Software Engineer for LPX System Software! NVIDIA’s LPX System Software team builds the foundational software that turns a novel deterministic compute architecture into a platform that compiler teams and data center operators can rely on. We shift complexity out of silicon and into software: the hardware abstraction layers, core system libraries, drivers, and runtime components that workloads enter the platform through. We build this stack in Rust. For system software living at the boundary between hardware and everything above it, we treat memory safety, explicit ownership, and long-lived API stability as the baseline rather than the goal — the foundation that lets us spend our judgment on the hard problems instead of on classes of bugs that should not exist.
As one of the principal engineers on this stack, set technical direction for the surfaces you own and shape the overall architecture alongside your fellow principals. Design the HAL, runtime interfaces, and data-movement pipelines the rest of the platform depends on; drive the hardest reliability and bring-up problems to root cause; and raise the throughput of the whole org by codifying the abstractions, patterns, and tooling that others build on. You will also help define how we engineer. We treat AI coding agents as a primary part of the workflow, and we expect our most senior engineers to be fluent in directing them — designing systems that are legible to both humans and agents, and turning hard-won judgment into leverage across the team.
What you’ll be doing:
Shape the architecture of the hardware abstraction layers and core system libraries, and own the API contracts for the components you lead.
Design and implement drivers, runtimes, and data movement and aggregation pipelines that execute workloads on novel silicon.
Build runtime interfaces for launching, monitoring, and managing workloads at production scale.
Drive triage of the most difficult sequencing, initialization, and cross-component runtime failures, and produce root-cause analyses that change how the system is built.
Lead new platform bring-up and NPI for new boards and silicon, in tight partnership with hardware engineering, compiler teams, and data center operations.
Multiply the team — establish the agent-assisted engineering practices, reusable abstractions, diagnostics, and documentation that let everyone move faster without destabilizing the platform.
Communicate architecture and design tradeoffs clearly, in writing and in diagrams, to audiences ranging from individual engineers to executive staff.
What we need to see:
MS in CS, CE, EE, or a related STEM field, or equivalent experience, and 12+ years building production system software.
Deep systems-programming expertise, with Rust as your language of choice for low-level work. You have shipped production Rust at the hardware or kernel boundary — drivers, firmware, runtimes, or similar — and you can articulate from experience where Rust earns its keep in system software and where it costs you. We work in Rust from day one; comfort is not enough, we want conviction.
A track record of designing and evolving libraries and APIs meant to be supported for years, including ABI and compatibility discipline.
Fluency in large, multi-repository codebases with layered dependencies.
Demonstrated leadership driving triage of difficult reliability issues to clear, written root-cause analysis.
Low-level platform experience: firmware and boot flows, RTOS, BMCs/MCUs, RISC-V, or closely related system software.
Linux driver or kernel-adjacent experience (for example, VFIO or similar subsystems).
Hardware bring-up and system triage experience: fault analysis, diagnostics, and validation in lab environments.
An established habit: building with AI coding agents — not as a novelty, but as a way you already ship and raise leverage. You can speak to how you design work to be agent-amenable and where you keep humans in the loop.
Ways to stand out from the crowd:
Experience having built Rust system software at the scale of a hyperscaler or a Rust-native hardware company — the kind of environment where Rust is the production language for low-level work, not an experiment.
Distributed systems experience: gRPC and RPC frameworks, coordination and telemetry patterns, MPI. Inference systems and token serving experience (vLLM or similar serving and runtime stacks) a huge plus.
Experience shipping and supporting customer-facing SDKs, including documentation and ABI compatibility practices.
Production readiness and delivery depth: CI/CD and release workflows, monitoring and alerting practices, Kubernetes, and data center operational workflows.
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 lead seeking new challenges? If so, we want to hear from you! Join us and help build the real-time, cost-effective AI inference and computing platform that's 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.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, 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.Skills Required
- MS in CS, CE, EE, or related STEM field, or equivalent experience, and 12+ years building production system software
- Deep systems-programming expertise with Rust; shipped production Rust at the hardware or kernel boundary (drivers, firmware, runtimes)
- Track record designing and evolving libraries and APIs for long-term support, including ABI and compatibility discipline
- Fluency in large, multi-repository codebases with layered dependencies
- Demonstrated leadership driving triage of difficult reliability issues and producing written root-cause analyses
- Low-level platform experience: firmware and boot flows, RTOS, BMCs/MCUs, RISC-V, or closely related system software
- Linux driver or kernel-adjacent experience (for example, VFIO or similar subsystems)
- Hardware bring-up and system triage experience: fault analysis, diagnostics, and validation in lab environments
- Established habit of building with AI coding agents and designing agent-amenable systems while keeping humans in the loop
- Experience building Rust system software at hyperscaler or Rust-native hardware company
- Distributed systems experience: gRPC and RPC frameworks, coordination and telemetry patterns, MPI, inference/serving stacks (vLLM etc.)
- Experience shipping and supporting customer-facing SDKs, documentation, and ABI compatibility practices
- Production readiness and delivery: CI/CD and release workflows, monitoring and alerting, Kubernetes, and data center operational workflows
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.
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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.”






