Principal Engineer - Perf and Benchmarking

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. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability.

Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. com .

About This Role

We’re looking for a Principal Engineer to be the technical lead of CoreWeave’s Benchmarking Performance team. You will be responsible for our planet-scale performance data warehouse: Ingesting, storing, transforming and analyzing performance events in all the data centers across our global infrastructure. You will also be an integral part of achieving industry-leading end-to-end performance benchmarking publications: If MLPerf (Training Inference), Working closely with NVIDIA (Megatron-LM, TensorRT-LLM DGX cloud) and the open-source community (llm-d, vLLM and all popular ML frameworks) speak to you, come help us demonstrate CoreWeave’s performance reliability leadership in the field.

What You’Ll Do

Strategy Leadership - Define the multi-year benchmarking strategy and roadmap; prioritize models/workloads (LLMs, diffusion, vision, speech) and hardware tiers. Build, lead, and mentor a high-performing team of performance engineers and data analysts. Establish governance for claims: documented methodologies, versioning, reproducibility, and audit trails.

Perf Ownership - Lead end-to-end MLPerf Inference and Training submissions: workload selection, cluster planning, runbooks, audits, and result publication. Coordinate optimization tracks with NVIDIA (CUDA, cuDNN, TensorRT/TensorRT-LLM, Triton, NCCL) to hit competitive results; drive upstream fixes where needed. Internal Latency Throughput Benchmarks - Design a Kubernetes-native, repeatable benchmarking service that exercises CoreWeave stacks across SUNK (Slurm on Kubernetes), Kueue, and Kubeflow pipelines.

Measure and report p50/p95/p99 latency, jitter, tokens/s, time-to-first-token, cold-start/warm-start, and cost-per-token/request across models, precisions (BF16/FP8/FP4), batch sizes, and GPU types. Maintain a corpus of representative scenarios (streaming, batch, multi-tenant) and data sets; automate comparisons across software releases and hardware generations. Tooling Automation - Build CI/CD pipelines and K8s controllers/operators to schedule benchmarks at scale; integrate with observability stacks (Prometheus, Grafana, OpenTelemetry) and results warehouses.

Implement supply-chain integrity for benchmark artifacts (SBOMs, Cosign signatures). Cross-functional Community - Partner with NVIDIA, key ISVs, and OSS projects (vLLM, Triton, KServe, PyTorch/DeepSpeed, ONNX Runtime) to co-develop optimizations and upstream improvements. Support Sales/SEs with authoritative numbers for RFPs and competitive evaluations; brief analysts and press with rigorous, defensible data.

Who You Are

10+ years building distributed systems or HPC/cloud services, with deep expertise on large-scale ML training or similar high-performance workloads. , telemetry platforms, observability stacks, cloud data warehouses, large-scale OLAP engines). Deep understanding of GPU performance (CUDA, NCCL, RDMA, NVLink/PCIe, memory bandwidth), model-server stacks (Triton, vLLM, TensorRT-LLM, TorchServe), and distributed training frameworks (PyTorch FSDP/DeepSpeed/Megatron-LM).

Proficient with Kubernetes and ML control planes; familiarity with SUNK, Kueue, and Kubeflow in production environments. Excellent communicator able to interface with executives, customers, auditors, and OSS communities.

Nice To Have

Experience with time-series databases, log-structured merge trees (LSM), or custom storage engine de