Staff Machine Learning Engineer (AI Insights)

Join CoreWeave, an AI-native cloud company, as a Staff Machine Learning Engineer. In this role, you will be a technical leader on the AI Insights team, defining and implementing machine learning systems that power various intelligence capabilities across our observability and cloud platforms. You will work across the full lifecycle of machine learning, from problem framing to model development, evaluation, and integration with production systems. You will also influence the technical direction of the team and help establish engineering and ML practices.

• Definir e implementar sistemas de aprendizado de máquina que alimentam a detecção de anomalias, correlação de sinais e recomendações.
• Trabalhar em todas as etapas do ciclo de vida do desenvolvimento, desde a definição de problemas até a operação em escala.
• Influenciar a direção técnica de uma equipe em crescimento na interseção de aprendizado de máquina, observabilidade e sistemas distribuídos.

• A track record of technical leadership, mentorship, and influence across organizational boundaries
• Experience working with time-series, event, log, metric, trace, or other operational data at meaningful scale
• Significant experience designing and shipping machine learning systems that operate in production
• 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
• Demonstrated ability to define evaluation methodology for ambiguous or domain-specific ML problems
• Deep understanding of machine learning fundamentals, including model selection, feature engineering, experimentation, evaluation, and failure analysis
• Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus
• 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 building ML products for infrastructure, developer tools, reliability engineering, or other technical users
• Experience with large language model evaluation, post-training, retrieval, tool use, or grounded generation—particularly when combined with structured telemetry and deterministic systems
• Familiarity with human-in-the-loop workflows, access control, auditability, and safety requirements for operational systems