Senior Machine Learning Engineer, AI Insights

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.

CoreWeave is an AI hyperscaler building the cloud infrastructure and services that power the next generation of artificial intelligence. Our customers run demanding training, inference, and high-performance workloads, and our teams build the systems needed to operate that infrastructure reliably at scale.

The AI Insights team builds customer-facing AI capabilities across CoreWeave’s Mission Control portfolio. We combine machine learning, observability, and production software engineering to help engineers and customers understand workload health, diagnose infrastructure issues, and identify opportunities to improve efficiency, capacity, and performance.

Our work spans telemetry from metrics, logs, traces, alerts, and operational events. We are building the intelligence layer that turns this data into trustworthy, actionable insights—grounded in evidence and integrated into the tools where people operate CoreWeave infrastructure.

This is not a role focused on building a generic chatbot. You will build the ML systems, services, evaluation frameworks, and product capabilities that make AI-powered troubleshooting and optimization reliable in real-world environments.

As a Senior Machine Learning Engineer, you will design, build, and operate machine learning capabilities that power observability, troubleshooting, and optimization experiences. You will work across the full lifecycle of ML development: understanding the problem, preparing data, developing models and algorithms, defining evaluation criteria, integrating with production services, and improving performance based on real-world feedback.

You will partner with software engineers, product managers, researchers, and infrastructure experts to turn ambiguous problems into reliable systems. You will have meaningful ownership of production components while contributing to the team’s technical direction and engineering practices.

• Build and ship production machine learning systems for infrastructure observability, troubleshooting, and optimization.

• Develop approaches for anomaly detection, time-series analysis, event correlation, ranking, recommendation, classification, and root-cause inference across high-volume telemetry.

• Create datasets, experiments, and evaluation frameworks to measure model quality, robustness, usefulness, and failure modes.

• Build data pipelines, feature-generation workflows, inference services, and feedback loops for continuous improvement.

• Integrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services.

• Collaborate with engineers working on metrics, logs, traces, telemetry enrichment, platform APIs, and data infrastructure.

• Monitor and improve the quality, latency, reliability, and cost of