Top Engineering Jobs in Seattle, WA

Leads technical due diligence for prospective data center sites, evaluating electrical, mechanical, civil, structural, architectural, utility, zoning, and infrastructure feasibility. Reviews provider proposals, capacity data, conceptual layouts, and building documentation; identifies risks, upgrades, cost and schedule impacts; prepares go/no-go recommendations and technical assessments. Coordinates with utilities, landlords, developers, internal subject matter experts, and design teams, then hands secured sites to Design Managers. Frequent travel is required.

Plans, procures, and manages backbone network capacity, including wavelength and dark fiber services. Reviews vendor solutions, forecasts network demand, optimizes circuit utilization, ensures redundancy and diversity, manages telecommunications vendors, maintains technical documentation, and supports network expansion. The role collaborates with engineering, operations, and product teams to resolve capacity issues and incorporate emerging technologies into global backbone planning.

Design and review data center white space infrastructure for high-density computing environments. Coordinate electrical, mechanical, cooling, low-voltage, cabling, and network pathway systems; review drawings, submittals, RFIs, and field conditions; oversee consultants; resolve design and constructability issues; and support construction, commissioning, and deployment. Contribute to design standards, scalability, technical documentation, and mentorship across CoreWeave’s data center portfolio.

Build and operate full-stack applications and AI-facing features for CoreWeave’s internal data platform. Responsibilities include developing TypeScript, React, Next.js, and Python services; designing APIs and relational data models; integrating analytical data platforms; deploying on Kubernetes; and owning production reliability, testing, observability, and on-call support. The role also involves collaborating with non-engineers to define processes, shipping LLM-backed applications, and evaluating AI output quality.

Build and ship full-stack software for CoreWeave’s internal data infrastructure and operational applications. Develop TypeScript, React, and Next.js frontends; Python services on Kubernetes; SQL queries and data models; and AI-enabled interfaces such as text-to-SQL tools, retrieval systems, and agents. Own scoped features through production, including testing and post-release fixes, while collaborating with senior engineers and internal users.

Serves as a technical lead for customers using CoreWeave cloud infrastructure, with emphasis on Kubernetes in high-performance computing environments. Responsibilities include designing and deploying tailored solutions, leading proofs of concept, optimizing AI/ML workloads, conducting technical reviews, advising on product strategy, collaborating with engineering teams, and representing CoreWeave at industry events.

Leads systems engineering teams developing high-throughput file, block, and object storage for AI cloud infrastructure. Drives architecture, performance tuning, latency optimization, benchmarking, and 10x scaling across distributed storage systems. Provides technical mentorship, performance management, recruitment, and cross-functional leadership while maintaining a coding-focused culture and maximizing GPU utilization.

Lead and develop an infrastructure engineering team responsible for internal Kubernetes platforms and foundational services. Set roadmaps, ownership, operating practices, and success metrics; guide reliable infrastructure and automation; establish testing, observability, SLO, incident response, change management, and on-call practices; resolve cross-team dependencies; and manage hiring, coaching, performance, priorities, and risk communication.

Design, build, and operate secure, high-performance sandboxed runtime environments for multi-tenant Kubernetes workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; integrate container runtimes, lightweight virtual machines, and virtualization technologies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, runtime, virtualization, GPU, and security architecture.

Build and operate internal support tooling for case triage, intelligent routing, diagnostics, incident workflows, and expert engagement. Develop AI-assisted automation, self-service diagnostics, observability dashboards, alerts, APIs, and Kubernetes-native controllers to improve reliability and reduce resolution time. Partner with Support/CX, Product, SRE, and engineering teams to prioritize initiatives, drive adoption, measure impact, and participate in on-call support.