Senior Software Engineer, Distributed Systems, EDA Infrastructure
New
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NVIDIAEDA infrastructure
United StatesFull-TimeSenior
SalaryThe base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
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Job Details
- Experience
- 5+ years of software engineering or infrastructure engineering experience supporting large-scale production systems.
- Required Skills
- PythonKubernetesGoLinuxDistributed Systems
Requirements
- Have 5+ years of software engineering or infrastructure engineering experience supporting large-scale production systems.
- Hold a BS in Computer Science, Engineering, Physics, Mathematics, or a related field, or have equivalent experience.
- Have strong programming experience in Go or Python, including data structures, algorithms, testing, and software design.
- Have experience designing automation for distributed systems and large fleets of Linux-based compute nodes.
- Understand performance, security, reliability, fault tolerance, state management, and data consistency in complex systems.
- Have experience with infrastructure automation, software deployment, observability, and operational recovery.
- Bring a systematic approach to problem solving, ownership, and reducing operational toil.
- Experience with large-scale EDA or high-performance computing infrastructure is a plus.
- Experience with workload schedulers or cluster-management platforms such as Slurm, LSF, Kubernetes, or Bright Cluster Manager is a plus.
- Experience with Linux, GPU and CPU server architecture, networking, storage, or bare-metal lifecycle management is a plus.
Responsibilities
- Design and build platforms to automate provisioning, configuration, operation, and lifecycle management of large-scale GPU and CPU compute infrastructure.
- Develop monitoring, health-management, and remediation systems for EDA compute environments.
- Automate hardware deployment, operating-system configuration, firmware and software updates, cluster enrollment, and recovery workflows.
- Build 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 on solutions for chip-design workloads.
- Participate in incident response, root-cause analysis, capacity planning, and continuous improvement of production services.
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