Infrastructure Software Engineer, Fleet & Automation

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About Nscale

Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers.  Nscale enables AI-focused companies to achieve superior results by reducing the complexity of AI development. Our GPU cloud bolsters technical capabilities and directly supports strategic business outcomes, including cost management, rapid innovation, and environmental responsibility.

We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you’ll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you’ll be contributing to building the technology that powers the future.

Overview

As an Infrastructure Software Engineer for Fleet & Automation, you will be a critical member of the AI Infrastructure Operations team, responsible for ensuring the acceptance, performance, and scalability of our cutting-edge AI and High-Performance Computing (HPC) environments. Leveraging software engineering principles, you will focus on building and maintaining the control plane, tooling, and automation that supports Fleet Operations, Network Operations, and Observability functions. Your work will directly translate into higher system availability and reduced operational costs.

Key Responsibilities

• Perform technical architecture, roadmap and implementation for workflow automation systems, driving architecture decisions that balance automation complexity, reliability, and maintainability. Identify and resolve performance and scalability issues. Establish technology and product direction in collaboration with other tech leads, managers, and senior leadership.
• Own end-to-end delivery of device provisioning, validation, testing, and remediation workflows at scale.
• Design and build workflow orchestration systems for hardware lifecycle management, including GPU nodes and network switches.
• Partner with Infrastructure, Platform, and SRE teams to translate operational needs into robust, scalable automation.
• Establish engineering standards for reliability, observability, and operational excellence across all services. Help set up engineering best practices in collaboration with the broader engineering team.
• Build production-grade Python systems for hardware lifecycle automation, leveraging AI tools to accelerate delivery. Assess impact to team software stack from new hardware product programs and explore AI driven process improvement and automation.
• Collaborate with cross-functional teams (product, design, operations, infrastructure) to build efficient, interoperable, and maintainable automated systems.

Required Qualifications

• Education: Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Experience: 5+ years relevant experience building large-scale infrastructure applications or similar experience.
• Programming: Experience in utilizing languages such as C, C++, Java, and scripting languages such as Python for API design and unit testing techniques.
• Systems Expertise: Deep understanding of Linux operating systems, networking fundamentals (TCP/IP, BGP), and familiarity with configuration management tools (e.g., Ansible, Terraform).
• Distributed Systems: Experience building, running and debugging large-scale infrastructure, stateful and stateless services for distributed systems or networks, and experience with compute technologies, storage, or hardware architecture. Experience integrating with infrastructure tooling such as: DCIMs, NetBox, OpenStack, bare metal APIs (MAAS, Ironic, IPMI).

Preferred Qualifications

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• Experience designing, analyzing and improving efficiency, scalability, and performance of various system resources.
• Direct experience with AI/HPC infrastructure, including NVIDIA GPUs, InfiniBand or high-speed Ethernet fabrics, and related management software (e.g., NCCL, SLURM).
• Experience with advanced observability and monitoring systems (Prometheus, Grafana, OpenTelemetry) for complex, high-cardinality telemetry data.
• Familiarity with cloud-native technologies (Kubernetes, Docker) and infrastructure-as-code principles.
• Demonstrated ability to integrate AI to