Staff 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 . About CoreWeave 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. About the team 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 help build the underlying ML systems, services, evaluation frameworks, and product capabilities that make AI-powered troubleshooting and optimization reliable in real-world environments. About the role As a Staff Machine Learning Engineer, you will be a technical leader on the AI Insights team. You will define and implement the machine learning systems that power anomaly detection, signal correlation, incident understanding, recommendations, and other intelligence capabilities across CoreWeave’s observability and cloud platforms. You will work across the full lifecycle: framing problems, developing models and algorithms, designing evaluation methodology, building data and inference services, integrating with production systems, and operating the result at scale. You will partner closely with software engineers, product managers, researchers, and domain experts to turn promising ideas into reliable customer experiences. What you’ll do Lead the technical design and delivery of 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. Build evaluation frameworks and datasets that measure accuracy, usefulness, robustness, and safety in real operational scenarios. Translate research and prototypes into maintainable, scalable services with clear operational ownership. Design data pipelines, feature-generation workflows, model-serving paths, and feedback loops for continuous improvement. Integrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services. Establish practical standards for experimentation, offline and online evaluation, monitoring, reproducibility, and model lifecycle management. Make thoughtful trade-offs across model quality, latency, cost, interpretability, reliability, and ease of operation. Work with teams responsible for metrics, logs, traces, telemetry enrichment, and platform APIs to create coherent cross-system intelligence. Mentor engineers and raise the technical bar for machine learning and production engineering across the organization. Communicate technical decisions clearly and influence roadmaps across teams without relying on formal authority. What we’re looking for Significant experience designing and shipping machine learning systems that operate in production. Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus. Deep understanding of machine learning fundamentals, including model selection, feature engineering, experimentation, evaluation, and failure analysis. Experience working with time-series, event, log, metric, trace, or other operational data at meaningful scale. Demonstrated ability to define evaluation methodology for ambiguous or domain-specific ML problems. Strong systems thinking and the ability to reason about distributed systems, data quality, latency, observability, and operational failure modes. Excellent communication and collaboration skills, including the ability to work effectively with research, product, infrastructure, and customer-facing teams. A track record of technical leadership, mentorship, an