Senior Data & MLOps Engineer

Responsibilities

• Design and implement scalable data ingestion pipelines
• Build feature processing and baseline computation systems
• Productionize models for prediction and detection
• Develop and operate low-latency services and robust offline workflows
• Architect horizontally scalable services using orchestration technologies
• Implement monitoring and feedback loops for continuous model improvement
• Collaborate with Platform teams to integrate operational signals into diagnostics
• Implement scalable solutions for mitigation and structured analysis

Requirements

• 7+ years of experience in data engineering, distributed systems, MLOps, or infrastructure ML
• Proven experience building high-throughput streaming or telemetry pipelines (Kafka, Pulsar, Kinesis)
• Strong experience designing time-series feature pipelines and large-scale observability systems
• Experience building and maintaining feature stores
• Hands-on experience deploying ML models to production (versioning, monitoring, drift detection)
• Experience designing scalable microservices in Kubernetes-based environments
• Strong proficiency in Python and at least one systems language (Go, Rust, or C++)
• Experience with distributed compute or training systems (NCCL, PyTorch Distributed, Spark, Ray, Slurm)
• Familiarity with GPU telemetry systems such as NVML or DCGM

Preferred Qualifications

• Experience working on GPU fleet management or AI training clusters
• Experience building anomaly detection or failure prediction systems for hardware
• Experience implementing distributed straggler detection systems
• Experience developing agentic or LLM-powered reasoning systems for diagnostics
• Background in reliability engineering or SRE practices

About the Company

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.