Job Opportunities API

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 Monolith Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet, Infrastructure, and AI Platform teams to embed data science directly into production environments—improving cluster reliability, increasing effective utilization (MFU), reducing MTTR, and protecting uptime and revenue. About the role: As a Data Scientist, you will work at the intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry data, and build predictive and optimization solutions that are tightly integrated into production workflows. This role blends hands-on deployment with analytical rigor, turning complex infrastructure data into measurable improvements in performance and cost. You will translate research and modeling insights into scalable, production-ready systems. Who You Are: MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related quantitative field 8+ years (or equivalent experience) applying statistical modeling or machine learning to large-scale datasets Strong proficiency in Python and scientific computing libraries (NumPy, pandas, SciPy, scikit-learn, PyTorch or TensorFlow) Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing) Experience working with distributed data systems (Spark, Ray, Dask, or similar) Proficiency in SQL and working with large-scale structured datasets Experience building, deploying, and maintaining predictive models in production environments Strong understanding of optimization techniques (linear programming, convex optimization, stochastic optimization, or reinforcement learning) Experience working with time-series data and performance telemetry Ability to translate analytical insights into production-ready systems and workflows Strong collaboration skills and ability to work cross-functionally in fast-paced environments Preferred: PhD with published research in systems optimization, distributed computing, ML systems, or performance modeling Experience with GPU workloads, distributed training, or AI infrastructure Familiarity with Kubernetes, containerized workloads, or cloud-native systems Experience deploying reinforcement learning or adaptive scheduling systems in production Background in capacity planning, forecasting, or resource allocation modeling Contributions to open-source ML or systems projects 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