CoreWeave
Staff Machine Learning Engineer, AI Insights
$180K–$250K
New York, NY +1 · Staff · Full-time · Posted Jun 18, 2025
- Location
- New York, NY
Manhattan, New York, United States - Experience
- Significant experience; no number of years specified.
- Visa sponsorship
- Applicants must be a U.S. person, eligible to access export-controlled information without export authorization, or eligible and reasonably likely to obtain the required U.S. government export authorization. CoreWeave may decline to pursue export licensing for legitimate business reasons.
About the job
Leads the design and delivery of production machine-learning systems that turn infrastructure telemetry into reliable insights for troubleshooting, anomaly detection, and optimization.
What you'll do
- Build and operate ML systems, data and inference services, evaluation frameworks, and feedback loops for observability and cloud platforms.
- Develop methods for anomaly detection, time-series analysis, event correlation, recommendations, classification, and root-cause inference.
- Provide technical leadership, mentor engineers, and influence ML practices and roadmaps across teams.
What they ask for
- Significant experience designing and shipping production machine-learning systems.
- Strong Python software engineering skills.
- Deep ML fundamentals and experience with operational data at meaningful scale.
- Ability to define evaluation methods for ambiguous ML problems; strong systems thinking and technical leadership.
Nice to have
- Experience with observability technologies such as Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, or Kafka.
- Experience with Kubernetes and cloud infrastructure, anomaly detection or incident intelligence, or LLM evaluation and grounded generation.
Pay
$180,000 to $250,000 USD (base salary)
Tools
Python, Go, Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, Kafka
AI focus
Builds ML capabilities for telemetry-based anomaly detection, incident understanding, troubleshooting, recommendations, and infrastructure optimization; may involve LLM evaluation, retrieval, tool use, and grounded generation.
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