Senior Software Engineer - Distributed Systems Engineer, EDA Infrastructure

Sr Software Engineer - Distributed Systems Engineer, EDA Infrastructure

NVIDIA is hiring engineers to build and scale the infrastructure that supports our Electronic Design Automation (EDA) workloads. We are looking for engineers with strong programming skills, a deep understanding of distributed systems, experience operating large-scale production infrastructure, and excellent communication and planning abilities. You will help design reliable automation and platform services that manage large fleets of GPU-based and CPU-based compute systems used by engineering teams across NVIDIA.

The ideal candidate is comfortable working across software, operating systems, cluster schedulers, networking, storage, and physical hardware. You should enjoy solving complex operational problems, eliminating repetitive work through automation, and building systems that remain reliable as infrastructure grows. If you are creative, pragmatic, and motivated to improve how critical engineering workloads are delivered, we would like to hear from you.

What You Will Be Doing:

• Design and build platforms that automate the provisioning, configuration, operation, and lifecycle management of large-scale GPU and CPU compute infrastructure.

• Develop monitoring, health-management, and remediation systems that improve the reliability, availability, and utilization of EDA compute environments.

• Automate hardware deployment, operating-system configuration, firmware and software updates, cluster enrollment, and recovery workflows.

• Build reliable services and workflows that integrate with workload schedulers, infrastructure management systems, and observability platforms.

• Use hardware diagnostics, operating-system signals, scheduler data, and network and storage telemetry to identify failures and return unhealthy systems to service.

• Work with EDA, infrastructure, networking, storage, and hardware engineering teams to deliver scalable solutions for critical chip-design workloads.

• Participate in incident response, root-cause analysis, capacity planning, and the continuous improvement of production services.

What We Need To See:

• 5+ years of software engineering or infrastructure engineering experience supporting large-scale production systems.

• A BS in Computer Science, Engineering, Physics, Mathematics, or a related field, or equivalent experience.

• Strong programming experience in Go or Python, including a solid understanding of data structures, algorithms, testing, and software design.

• Experience designing automation for distributed systems and large fleets of Linux-based 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 skills and the ability to work effectively across teams, organizations, and geographic regions.

• A systematic approach to problem solving, with a strong sense of ownership and an emphasis on reducing operational toil.

Ways To Stand Out From The Crowd:

• Experience designing or operating large-scale EDA or high-performance computing infrastructure. Deep knowledge of Linux, GPU and CPU server architecture, networking, storage, and bare-metal lifecycle management.

• Hands-on experience with workload schedulers and cluster-management platforms such as 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 and operating-system upgrade workflows, or node-provisioning systems.

• A track record of improving infrastructure reliability, utilization, and recovery time through production-quality automation. Ex...