Top Tech Jobs & Startup Jobs in Seattle, WA

Designs and operates virtualization and Kubernetes orchestration platforms for large-scale GPU and HPC workloads. Responsibilities include GPU cluster provisioning, workload scheduling, resource allocation, multi-tenant infrastructure, cluster lifecycle management, automation, reliability, security, and scalability. The role partners with hardware, networking, infrastructure, and AI platform teams while owning complex systems from architecture through production operations.

Conduct applied research on AI models, inference systems, accelerator ecosystems, and data center infrastructure. Evaluate emerging technologies and translate findings into engineering, product, infrastructure, financial, and commercial strategies. Own technical views on GPU hall capacity, power, cooling, density, cost, and performance. Support customer and partner discussions, infrastructure diligence, benchmarking, and technology investment decisions. The role requires hands-on inference, compiler, kernel, runtime, and multi-accelerator experience, with up to 25% international travel.

Optimize GPU kernels and data-plane performance for large-scale AI workloads by profiling, identifying bottlenecks, tuning kernels, developing benchmarks and tooling, and collaborating with ML and platform teams to maximize GPU utilization, throughput, and reliability.

Optimize GPU kernels and data-plane performance for large-scale AI training and inference. Profile workloads, identify bottlenecks, improve latency, throughput, and utilization, develop benchmarking practices, and evaluate GPU technologies across distributed infrastructure. Collaborate with AI infrastructure, machine learning, and platform engineering teams to improve GPU efficiency, scalability, and reliability.

Optimize GPU kernels and data-plane performance for large-scale AI training and inference workloads. Profile bottlenecks, improve utilization, latency, throughput, and scalability, develop benchmarking practices, evaluate GPU technologies, and collaborate with infrastructure, machine learning, and platform engineering teams.

Lead the architecture, mechanical design, configuration, validation, and production deployment of GPU servers and rack-scale AI infrastructure. Partner with NVIDIA, AMD, ODMs, OEMs, data center engineering, networking, and operations teams. Own rack layouts, power distribution, cooling, airflow, cable management, serviceability, qualification, and hardware standards while balancing performance, reliability, manufacturability, scalability, and cost.

Build and operate production-grade AI model-serving and inference systems. Optimize large-model workloads for throughput, latency, GPU utilization, scalability, reliability, and cost. Collaborate with training, GPU performance, orchestration, and infrastructure teams, while developing monitoring, alerting, and operational practices. Investigate performance and capacity issues and contribute to platform architecture and engineering standards.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, resource utilization, training pipelines, developer tooling, and operational processes. The role partners with platform, orchestration, performance, and machine learning teams to diagnose training issues and support production-ready AI workloads.

Leads large-scale AI infrastructure and data center deployment programs from planning through commissioning and production handoff. Coordinates engineering, facilities, supply chain, vendors, and contractors across GPU infrastructure, networking, storage, power, cooling, and site readiness. Owns schedules, dependencies, risks, technical acceptance, readiness, executive reporting, and operational handoff. Requires strong technical credibility, deployment experience, vendor accountability, and readiness to travel up to 50%.

Build and scale distributed training infrastructure for large AI models across GPU clusters. Improve training reliability, efficiency, fault tolerance, checkpointing, and resource utilization. Integrate training systems with production ML pipelines, develop tooling and automation to improve developer experience, diagnose training throughput and stability issues, and establish operational best practices for a growing AI infrastructure platform.