Platform Engineer - Distributed Systems (Equity Eligible)

NVIDIA; United States; Remote; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA seeks a Sr Software Engineer to design and operate distributed systems that power our EDA infrastructure. You will automate provisioning, configuration, and lifecycle management for large GPU and CPU compute fleets, while building monitoring and remediation with a focus on reliability.

The role requires 5+ years in software or infrastructure engineering, strong Go or Python skills, and experience with Linux, schedulers, and hardware-aware systems.

Qualifications

• 5+ years of software or infrastructure engineering for large-scale production systems.
• BS in Computer Science, Engineering, Physics, Mathematics, or related field, or equivalent experience.
• Strong programming in Go or Python with data structures and algorithms.
• Experience designing automation for distributed systems and large compute fleets.
• Understanding of performance, security, reliability, fault tolerance, and data consistency.

Responsibilities

• Design and build platforms to automate provisioning, configuration, operation, and lifecycle of large-scale GPU/CPU infrastructure.
• Develop monitoring, health-management, and remediation systems for reliable environments.
• Automate hardware deployment, OS configuration, firmware/software updates, and recovery workflows.
• Build services that integrate with schedulers, management systems, and observability platforms.
• Collaborate with EDA, infrastructure, networking, storage, and hardware teams to deliver scalable solutions.

Skills

Go

Python

Distributed systems

Automation

Communication

Ownership

Go and Python

Education

BS in Computer Science, Engineering, Physics, Mathematics, or related field

Equivalent experience

Tools

Slurm

LSF

Kubernetes

Bright Cluster Manager

Job description

NVIDIA seeks a Sr Software Engineer to design and operate distributed systems that power our EDA infrastructure. You will automate provisioning, configuration, and lifecycle management for large GPU and CPU compute fleets, while building monitoring and remediation with a focus on reliability.

The role requires 5+ years in software or infrastructure engineering, strong Go or Python skills, and experience with Linux, schedulers, and hardware-aware systems.