Top Engineering Jobs in San Francisco, CA

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.

Build and operate CoreWeave’s multi-cloud internal infrastructure across GCP, Azure, and AWS. Manage cloud foundations, IAM, networking, Terraform infrastructure as code, monitoring, logging, CI/CD, backups, disaster recovery, and production reliability. Troubleshoot complex infrastructure and application issues, support cloud migrations and acquisitions, implement secure architecture patterns, and contribute to incident response and root-cause analysis. Mentor junior engineers and communicate technical decisions across teams.

As a Solutions Architect, engage with customers to architect cloud solutions, optimize workloads, and provide technical leadership while collaborating with engineering teams.

As a Solutions Architect at CoreWeave, you'll lead customer engagement, optimize cloud infrastructure solutions, and collaborate closely with engineering teams on product development and enhancements.

Lead and expand the SRE/Production Engineering team to ensure platform reliability, performance, and operational efficiency. Drive SRE strategy, automation-first practices (IaC, Terraform, Kubernetes), incident management, postmortems, SLO/SLA frameworks, on-call strategy, and system hardening. Collaborate with engineering, product, hardware, and security teams to build scalable, resilient, self-healing AI-focused cloud infrastructure.