UK remote
Staff Software Engineer, Runtime Systems
About this role
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025.
Learn more at www.coreweave.com . We're proud to be a Living Wage accredited Employer. What You’ll Do: The Physical AI Engineering Team at CoreWeave is building the software and infrastructure that enables demanding AI, simulation, robotics, and engineering workloads to run reliably at scale. As these workloads become more complex, the challenge is no longer simply providing compute. We need to make heterogeneous workloads easier to execute, observe, reproduce, and move across different systems without hiding the capabilities or semantics of the infrastructure underneath them.
About the role: We’re seeking a Staff Software Engineer, Runtime Systems to help design and build this layer. This is a hands-on systems engineering role at the intersection of distributed systems, runtimes, workflow execution, programming language concepts, and large-scale compute infrastructure . You’ll work on the foundations that allow complex workloads to move from an abstract description into reliable execution across systems such as Kubernetes, Argo, OSMO, and future execution environments.
A major part of the role is deciding where abstraction is useful — and where it creates more complexity. Rather than building another universal workflow engine, you’ll help establish clear contracts between our platform and the systems that execute work, while preserving native capabilities. You’ll operate across architecture and implementation: defining contracts, writing production software, validating assumptions against real workloads, and working closely with platform and infrastructure teams.