Principal Systems Engineer

Nscale•Seattle, WA

8d•$175,000 - $225,000

About The Position

We are building AI infrastructure for frontier-scale workloads. Our platform is designed for high-density, high-performance GPU clusters that push the limits of power, networking, and distributed compute. As a startup, we move fast, operate with ownership, and expect technical leaders to define standards—not just follow them. We are hiring a Principal Deployment Engineer to architect and lead the bringup of large-scale GPU clusters (hundreds to thousands of GPUs). This is a technical leadership role responsible for defining how we deploy, validate, and scale AI superclusters across sites. You will own the full lifecycle of deployment—from rack design and fabric architecture to cluster validation frameworks and production readiness standards. You will set the bar for performance, reliability, and operational excellence. This role combines deep hands-on expertise with system-level thinking and cross-functional leadership.

Requirements

10+ years of experience in large-scale infrastructure or HPC environments.
Proven experience bringing up large GPU clusters (hundreds+ GPUs).
Deep expertise in high-speed networking (InfiniBand, RoCE, Ethernet fabrics).
Strong understanding of server architecture (PCIe, NUMA, memory hierarchy).
Experience debugging performance issues across compute and network layers.
Strong automation and systems-level thinking.

Nice To Haves

Experience scaling AI training clusters for frontier models.
Experience with liquid cooling or ultra-high-density deployments.
Knowledge of distributed storage systems (Lustre, Ceph, NVMe-oF).
Experience defining infrastructure standards in a fast-growing organization.

Responsibilities

Define the technical standards for node, rack, and full-cluster bringup.
Lead large-scale GPU cluster deployments (multi-rack, multi-pod environments).
Architect high-performance network fabrics (IB, RoCE, Ethernet) optimized for AI workloads.
Establish cluster-level acceptance criteria and validation frameworks.
Tune and validate NCCL, RDMA, GPUDirect, and collective operations at scale.
Identify and eliminate performance bottlenecks across hardware, topology, and firmware layers.
Drive congestion control and fabric optimization strategies.
Define performance benchmarking methodology for AI training workloads.
Design repeatable deployment models for multi-site expansion.
Build automation frameworks for provisioning and cluster validation.
Establish deployment SLAs, quality gates, and operational readiness standards.
Reduce time-to-capacity while increasing reliability.
Serve as the escalation point for complex bringup and performance issues.
Mentor senior engineers and shape infrastructure best practices.
Influence hardware selection, rack topology, and data center design decisions.
Partner with executive leadership on infrastructure scaling strategy.