Senior Software Engineer, Distributed Systems Engineer, EDA Infrastructure

Nvidia

Confirmed live 2 days ago High trust

Remote

Quick summary

Work type: Remote
Location: WAWestford, MAAustin, TXDurham, NC
Salary: $152,000–$241,500 / yr
Posted: 3 days ago
Freshness: Confirmed live 2 days ago
Nearby: 37 roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $163k

This role $197k

$102k most similar roles pay here $256k

This role pays more than 83% of similar roles. Most pay $149,350–$177,250 — the shaded band above. At the midpoint, this role pays about $197k versus about $163k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Software Engineer, Distributed Systems Engineer, EDA Infrastructure

Role Posting Log in to save

Senior Software Engineer - Distributed Systems Engineer, EDA Infrastructure joins the team to build and scale infrastructure supporting Electronic Design Automation workloads. This role involves designing and building platforms to automate the provisioning, configuration, operation, and lifecycle management of large-scale GPU and CPU compute systems. The engineer will develop monitoring, health-management, and remediation systems while automating hardware deployment, firmware updates, and recovery workflows. Key responsibilities include integrating with workload schedulers and observability platforms using data from network, storage, and hardware diagnostics to ensure reliability for critical chip-design workloads. Candidates should possess strong programming skills in Go or Python, along with experience in distributed systems, Linux-based compute nodes, and infrastructure automation. The role addresses the technical challenge of managing complex, high-performance computing environments while reducing operational toil through production-quality software and automated system management across heterogeneous hardware.

Skills

Go Python Linux Kubernetes Slurm LSF Bright Cluster Manager Distributed Systems Infrastructure Automation Networking Storage Observability Capacity Planning

What you'll do

Design and build platforms to automate the provisioning, configuration, and lifecycle management of large-scale GPU and CPU compute infrastructure.
Develop monitoring, health-management, and remediation systems to improve the reliability and availability of EDA compute environments.
Automate hardware deployment, operating-system configurations, firmware updates, and cluster enrollment workflows.
Build reliable services that integrate with workload schedulers, infrastructure management systems, and observability platforms.
Analyze hardware diagnostics, OS signals, and network telemetry to identify failures and restore unhealthy systems to service.
Perform incident response, root-cause analysis, and capacity planning for production services.
Develop scalable solutions for critical chip-design workloads by integrating with networking, storage, and hardware teams.

What we're looking for

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 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 and geographic regions.
A systematic approach to problem solving with a focus on reducing operational toil.

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