Senior Software Engineer (AI Infrastructure Performance Insights & Observability)

Join CoreWeave, a leading cloud provider for AI workloads. As a Senior Software Engineer, you will be a key player in our Benchmarking & Performance team, focusing on building performance insights and observability systems for our AI infrastructure. You will own the detection, diagnosis, and surfacing of performance signals across our global infrastructure, turning raw telemetry events into actionable insights. This role requires strong coding skills in Python or Go, experience with distributed systems, and a deep understanding of networked systems and performance analysis.

• Design and build systems that continuously assess AI infrastructure health and performance, including GPU utilization and efficiency.
• Own and extend the time-series database (TSDB) layer as the backbone of real-time observability, writing and optimizing queries for alerting and anomaly detection.
• Build and validate pipelines and metrics that make network fabric and GPU-level behavior observable and comparable across racks, clusters, and hardware generations.

• Hands-on experience with Kubernetes at production scale, CI/CD, and observability stacks (Prometheus, Grafana, OpenTelemetry) used to monitor and diagnose infrastructure, not just report on it
• Familiarity with data lake architectures and modern table formats (Iceberg, Parquet, Avro) sufficient to support an insights platform, though this is not the primary skill this role is hiring for
• Comfortable working close to hardware and workload behavior: GPU utilization patterns, interconnect fabric health, distributed training/inference performance characteristics
• 5+ years of experience building distributed systems, observability platforms, or performance engineering tooling, ideally for infrastructure or ML systems rather than general-purpose BI
• Strong coding in Python or Go (C++ a plus) and deep familiarity with networked systems, GPU infrastructure, and performance analysis
• Strong communicator comfortable collaborating with cross-functional teams and external partners
• Working knowledge of time-series databases and fluency in PromQL or MetricsQL for building real-time alerting and anomaly detection, not only historical dashboards
• Experience with time-series databases, LSM-based storage engines, or custom telemetry pipelines
• Experience running MLPerf submissions or similar large-scale audited benchmarks
• Contributions to OSS projects such as Apache Iceberg, Apache Spark, Trino, llm-d, vLLM, or PyTorch
• Direct experience benchmarking or monitoring large GPU fleets or multi-region clusters
• Experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies
• Familiarity with data cataloging, lineage tools, or data governance frameworks