Top Tech Jobs & Startup Jobs in Seattle, WA

Build, validate, and operate large-scale bare-metal GPU infrastructure for AI/ML and HPC workloads. Responsibilities include managing Linux systems, image pipelines, test clusters, provisioning, firmware and driver validation, GPU diagnostics, performance analysis with NVIDIA DCGM, automation, virtualization, and hardware management interfaces. The role requires troubleshooting across hardware and software layers while collaborating with infrastructure, hardware, data center, platform, and ML teams.

Operate and scale large GPU infrastructure platforms, including Linux systems, bare-metal environments, provisioning workflows, observability, and reliability automation. Responsibilities include platform deployment, incident response, break/fix operations, customer provisioning, on-call participation, infrastructure troubleshooting, and collaboration across engineering, networking, customer success, and software teams. The role also develops automation to reduce manual work and improve operational efficiency.

Leads complex, cross-functional infrastructure programs spanning compute, networking, storage, observability, hardware, and datacenter operations. Owns program planning, milestones, dependencies, risks, and execution across multiple workstreams. Partners with engineering, operations, security, platform, and leadership teams to deliver network expansions, datacenter growth, hardware rollouts, GPU platform deployments, and infrastructure readiness. Provides leadership updates and improves execution frameworks, tooling, and program visibility.

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Develop and scale the Lightning AI platform across frontend, CLI, APIs, and backend systems. Build features using React, Python, or Go; improve stability, performance, architecture, automation, and continuous delivery. Collaborate with engineering, product, and design leaders, own end-to-end feature development, reduce technical debt, and mentor engineers on system design and problem-solving.

Support ML engineers running large-scale training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, networking, storage, scheduling, performance, and reliability issues. Analyze observability data, guide customers through complex infrastructure problems, support production incidents, and improve platform reliability through automation, tooling, documentation, runbooks, and operational processes. The role partners closely with infrastructure, networking, and platform engineering teams in a hybrid office environment.

Develop and scale the Lightning AI platform’s frontend and UI infrastructure using React and Redux. Build end-to-end features, improve stability and performance, evaluate technical architecture, automate software delivery, reduce technical debt, and mentor engineers. Collaborate with engineering, product, and design teams in a rapidly changing SaaS environment while maintaining high standards for code quality and system design.

Design and scale reliable spine-leaf data center networks supporting GPU clusters and AI workloads. Build Ethernet fabrics using EVPN/VXLAN and BGP, optimize traffic, support RoCE/RDMA, and manage backbone, WAN, DCI, and edge connectivity. Develop network automation and Infrastructure-as-Code solutions, improve observability and telemetry, troubleshoot distributed performance issues, and collaborate with compute, storage, platform, and operations teams. The role requires hands-on Cumulus NOS expertise and experience with large-scale, high-performance infrastructure.