Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Kubernetes @ 4
Linux @ 4
CI/CD @ 4
Distributed Systems @ 4
Leadership @ 4
gRPC @ 4
Rust @ 4
API @ 4
AI @ 4
vLLM @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA’s LPX System Software team builds the foundational software that turns a deterministic compute architecture into a platform compiler teams and data center operators can rely on. The team shifts complexity out of silicon and into software: hardware abstraction layers, core system libraries, drivers, and runtime components. The stack is built in Rust with an emphasis on memory safety, explicit ownership, and long-lived API stability.
Responsibilities
- Shape the architecture of hardware abstraction layers and core system libraries, and own 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 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 partnership with hardware engineering, compiler teams, and data center operations.
- Establish agent-assisted engineering practices, reusable abstractions, diagnostics, and documentation to raise team throughput without destabilizing the platform.
- Communicate architecture and design tradeoffs clearly, in writing and diagrams, to audiences ranging from individual engineers to executive staff.
Requirements
- 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 the language of choice for low-level work; shipped production Rust at the hardware or kernel boundary (drivers, firmware, runtimes, or similar).
- 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.
- Established habit of building with AI coding agents; able to design systems that are agent-amenable and know where humans must remain in the loop.
Ways to stand out
- Experience building Rust system software at hyperscaler scale or at a Rust-native hardware company where Rust is the production language for low-level work.
- 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).
- 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.
Compensation & Benefits
- Base salary range: 272,000 USD - 431,250 USD (determined based on location, experience, and pay of employees in similar positions).
- Eligible for equity and benefits (link provided in original posting).
Additional information
- Applications accepted at least until July 4, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer committed to fostering an inclusive work environment.