Machine Learning Engineer

About the company

• CoreWeave is an AI hyperscaler building the cloud infrastructure and services that power the next generation of artificial intelligence
• Publicly traded company (Nasdaq: CRWV) since March 2025

About the team

• The AI Insights team builds customer-facing AI capabilities across CoreWeave's Mission Control portfolio
• Combines machine learning, observability, and production software engineering
• Work spans telemetry from metrics, logs, traces, alerts, and operational events

Responsibilities

• 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
• Build evaluation frameworks and datasets measuring accuracy, usefulness, robustness, and safety
• Design data pipelines, feature-generation workflows, model-serving paths, and feedback loops
• Integrate ML capabilities with telemetry platforms including Mission Control, Grafana, and related observability services
• Establish standards for experimentation, offline and online evaluation, monitoring, reproducibility, and model lifecycle management
• Mentor engineers and raise the technical bar for machine learning

Requirements

• Significant experience designing and shipping production machine learning systems
• Strong software engineering skills in Python; Go or another systems-oriented language is a plus
• Deep understanding of ML fundamentals: model selection, feature engineering, experimentation, evaluation, failure analysis
• Experience with time-series, event, log, metric, trace, or other operational data at scale
• Demonstrated ability to define evaluation methodology for ambiguous or domain-specific ML problems
• Strong systems thinking and reasoning about distributed systems, data quality, latency, observability
• Excellent communication and collaboration skills
• Track record of technical leadership, mentorship, and influence

Nice to have

• Experience with Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, or Kafka
• Experience with Kubernetes and cloud infrastructure
• Experience with anomaly detection, incident intelligence, search, recommendations, or ranking systems
• Experience with LLM evaluation, post-training, retrieval, tool use, or grounded generation
• Familiarity with human-in-the-loop workflows, access control, auditability, and safety requirements