Staff Machine Learning Engineer

Who you are

• 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, and influence across organizational boundaries
• Experience with observability platforms or technologies such as Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, or Kafka
• Experience with Kubernetes and cloud infrastructure, especially for telemetry, logging, or application observability
• Experience with anomaly detection, incident intelligence, search, recommendations, or ranking systems
• Experience with large language model evaluation, post-training, retrieval, tool use, or grounded generation—particularly when combined with structured telemetry and deterministic systems
• Experience building ML products for infrastructure, developer tools, reliability engineering, or other technical users
• Familiarity with human-in-the-loop workflows, access control, auditability, and safety requirements for operational systems

What the job involves

• 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
• Work on foundational AI capabilities for an AI-native cloud company
• Solve difficult ML problems using high-volume, high-value infrastructure telemetry
• Help define how engineers and customers understand, troubleshoot, and optimize large-scale AI workloads
• Influence the technical direction of a growing team at the intersection of machine learning, observability, and distributed systems
• Turn applied research and engineering ideas into production products used by CoreWeave engineers and customers
• Lead by example while helping establish the team’s engineering and ML practices

The application process

• Some roles at CoreWeave require a live paired coding exercise in Python or Go