Simulating how a jet engine handles turbulence used to be a pure engineering problem. Increasingly, it’s also a computing problem, and that shift is exactly what’s pulling Rescale toward CoreWeave.
Rescale, a digital engineering platform used across aerospace, automotive, energy, life sciences, and manufacturing, announced this week that it’s expanding onto CoreWeave Cloud to handle AI and simulation workloads. The move links Rescale’s platform, which pulls together compute infrastructure, simulation software, and AI tools for R&D teams, with CoreWeave’s infrastructure that targets generative AI and high-performance computing from the ground up.
Through CoreWeave Kubernetes Service, Rescale customers get access to containerized orchestration with high-performance networking and storage, letting engineering teams run distributed simulations and AI models without managing the infrastructure underneath themselves.
The reason this integration matters comes down to how engineering work itself is changing. “As Rescale customers move deeper into AI physics and agentic engineering workflows, the compute demands are fundamentally different from traditional simulation,” said John Moonshower, Rescale’s chief revenue officer. Traditional simulation software targeted one kind of workload and never ventured beyond it. AI-driven engineering, where models help predict outcomes or automate parts of the design process, needs something closer to what CoreWeave set out to build from the start.
CoreWeave’s chief revenue officer, Jon Jones, framed the partnership similarly, pointing to the demanding nature of the simulations Rescale customers run and describing the collaboration as giving those engineers access to infrastructure matched to that scale.
CoreWeave has built a reputation specifically around AI-optimized cloud infrastructure, supporting everything from early-stage experimentation and model training to production inference and large-scale simulation work. The company has also posted strong results on MLPerf benchmarks and holds top platinum rankings in both versions of SemiAnalysis’s ClusterMAX rating system, a widely used measure of AI cloud performance and reliability.
For engineering teams working on things like aircraft design, drug discovery, or next-generation vehicle systems, the practical upside is straightforward: less time spent managing servers and infrastructure, more time running the actual simulations that determine whether a product works. As AI keeps working its way deeper into engineering and physical simulation, partnerships like this one signal where cloud providers see the next wave of serious computing demand coming from.
