Senior Applied Researcher

Job description

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 Monolith 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 a Senior Applied Researcher, you will take end-to-end ownership of complex machine learning problems across the Physical AI space. This is a high-impact role for someone with a strong technical foundation and a generalist mindset who can move from problem framing and experimentation through deployment and ongoing support. 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 research depth with practical delivery, and requires someone who can align stakeholders, surface risks and limitations, and communicate technical decisions clearly. Who You Are: 5+ years of experience in data science, machine learning, or applied AI, with evidence of delivering high-impact production ML systems. Strong software engineering skills in Python, with extensive experience using scientific computing and ML libraries such as NumPy, pandas, SciPy, scikit-learn, PyTorch, or TensorFlow. Experience deploying and supporting ML systems in 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 a…