Senior Applied Researcher

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. Experience contributing to the deployment or support of ML systems, or a good understanding of how models move from experimentation into production, including cloud-based environments. Strong grounding in statistical modelling, machine learning experimentation, and evaluation. Experience working with time-series, high-dimensional, or imperfect real-world datasets. Strong technical curiosity and a habit of keeping up with current ML and AI research. Comfortable working cross-functionally with engineering, product, and customer-facing teams, and able to explain technical decisions clearly to different audiences. Preferred: (if applicable) Background in engineering, physics, chemistry, or another STEM discipline connected to complex physical systems. Experience with anomaly detection, optimisation, reliability-focused modelling, or related approaches in industrial settings. Experience building or experimenting with agents or autonomous systems. Familiarity with MLOps, model monitoring, and efficient compute practices. Experience working with sparse, noisy, or imperfect real-world data from labs, heavy industry, or other applied environments. Track record of collaborating closely with deployed or client-facing teams to bring advanced methods into operational use. Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams - even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. You love applying advanced ML to real-world, high-value physical engineering problems. You're curious about agentic and autonomous approaches and how they can reshape engineering workflows. You enjoy t