Qualifications

• MS or PhD in Computer Science, Statistics, Applied Mathematics, Machine Learning, or a 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, 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 Qualifications

• 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

Responsibilities

• Develop and deploy advanced statistical models and machine learning algorithms within production environments to enhance system reliability and efficiency.
• Collaborate with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system performance in real time.
• Design and execute experiments, analyze large-scale telemetry data, and develop predictive and optimization solutions aligned with operational goals.
• Translate research insights into scalable, production-ready systems that improve infrastructure performance and reduce operational costs.
• Identify failure patterns and anomalies in infrastructure datasets, contributing to proactive system maintenance and reliability improvements.
• Embed data science solutions into existing workflows, ensuring seamless integration and operational effectiveness.
• Stay updated with the latest advancements in machine learning, distributed computing, and infrastructure optimization to continuously improve models and systems.
• Work closely with cross-functional teams to understand system requirements and translate them into data-driven solutions.

Benefits

• Family‑level Medical Insurance
• Family‑level Dental Insurance
• Generous Pension Contribution
• Life Assurance at 4x Salary
• Critical Illness Cover
• Employee Assistance Programme
• Tuition Reimbursement
• Work culture focused on innovative disruption

Equal Opportunity

CoreWeave is an equal opportunity employer committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.