$152,000 – $287,500
Listed on NVIDIA’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
NVIDIA is seeking a distributed systems engineer to design and operate automation platforms for their GPU and CPU compute infrastructure supporting chip-design workloads. The role combines software engineering with infrastructure operations, requiring expertise in Linux systems, large-scale deployment automation, and reliability engineering across geographically distributed environments.
Our summary, not NVIDIA’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- 5+ years of software or infrastructure engineering on large-scale production systems
- Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, or equivalent experience
- Strong programming in Go or Python with solid grasp of data structures, algorithms, testing, and software design
- Experience automating distributed systems and large fleets of Linux compute nodes
- Understanding of performance, security, reliability, fault tolerance, state management, and data consistency in complex systems
- Experience with infrastructure automation, software deployment, observability, and operational recovery
- Strong communication and cross-team collaboration skills
- Systematic problem-solving approach with ownership mindset and focus on reducing operational toil
Nice to have
- Experience designing or operating large-scale EDA or high-performance computing infrastructure
- Deep knowledge of Linux, GPU/CPU server architecture, networking, storage, and bare-metal lifecycle management
- Hands-on experience with Slurm, LSF, Kubernetes, or Bright Cluster Manager
- Experience supporting EDA applications, license-management systems, high-throughput batch workloads, or semiconductor design workflows
- Experience building automated health checks, break-fix remediation, firmware/OS upgrade workflows, or node-provisioning systems
- Track record of improving infrastructure reliability, utilization, and recovery time through production automation
- Experience operating infrastructure across multiple data centers or heterogeneous hardware environments