UK remote
Applied Researcher
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: CoreWeave’s Physical AI team is building the essential cloud platform for AI innovators, applying machine learning to solve intractable physics and engineering challenges. We operate at the intersection of advanced cloud infrastructure and applied data science, transforming how complex industrial products - such as automotive systems, aircraft, and advanced batteries - are conceived, tested, and developed.
About the role: As an Applied Researcher, you will help solve machine learning problems across the Physical AI space. This is a hands-on role for someone with a strong technical foundation and a generalist mindset who wants to contribute across problem framing, experimentation, deployment, and ongoing support, with guidance from experienced researchers and engineers. You will work closely with engineering, product, and client-facing teams to turn advanced ML methods into production-ready solutions that can be trusted in real customer environments.
The role blends applied research with practical delivery, and requires someone who can work collaboratively, surface risks and limitations, and communicate technical decisions clearly. Who You Are: Experience in data science, machine learning, or applied AI, gained through industry roles, applied research, or a combination of both, with evidence of applying ML to practical problems. Strong programming skills in Python, with practical experience using scientific computing and ML libraries such as NumPy, pandas, SciPy, scikit-learn, PyTorch, or TensorFlow.