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 . 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 build the 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 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. What you’ll do 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 ML-powered services in production. Investigate data and model failures, identify root causes, and implement durable fixes. Make thoughtful trade-offs across model quality, interpretability, operational complexity, latency, and cost. Contribute to technical designs, code reviews, testing standards, and operational practices for the team. Partner with Staff engineers and technical leaders to break down complex initiatives and deliver incrementally. Share knowledge through documentation, mentorship, and collaboration with engineers across CoreWeave. What we’re looking for Several years of 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. Solid 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. Experience building reliable data and inference services, not only notebooks or offline prototypes. Experience defining and implementing evaluation methodology for ambiguous or domain-specific ML problems. Strong debugging and systems-thinking skills, including the ability to reason about data quality, distributed systems, latency, and operational failure modes. Experience taking an ML capability from an initial hypothesis through production launch and iteration. Excellent communication and collaboration skills, w