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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineer for CoreWeave's Benchmarking & Performance team - **Company:** Coreweave, Inc. - **Location:** United States - **Experience:** Expert - **Salary:** $182,000.0 - $242,000.0 - **Contract:** Permanent contract - **Skills:** Adobe InDesign, C++ (Programming Language), Computer Clusters, Code Review, Nvidia CUDA, Computer Programming, Databases, Continuous Integration, Data Centers, Data Warehousing, Distributed Systems, Python (Programming Language), PCI Express, Remote Direct Memory Access, Cloud Services, Prometheus, Computer Networking Systems, High Performance Computing, Pytorch, Large Language Models, Grafana, Kubernetes, Low Latency, TensorRT, Data Pipelines - **Published:** August 2, 2026 - **Apply:** https://www.dice.com/job-detail/6986de1f-d124-40d7-b4d0-fa07ec667299 ## About the Role * 3-5 years of experience building distributed systems, high-performance computing components, or cloud services. * Strong programming skills in Python or Go (C++ a plus) with understanding of networked systems and performance fundamentals. * Hands-on experience with Kubernetes in production environments plus familiarity with CI/CD and observability tools (e.g., Prometheus, Grafana, OpenTelemetry). * Exposure to performance-critical GPU systems (CUDA, NCCL, NVLink/PCIe, memory bandwidth) or model-serving stacks (llm-d, vLLM, TensorRT-LLM, Megatron-LM). * Effective communicator comfortable working cross-functionally. Nice to have * Experience with time-series databases, LSM-based storage engines, or custom data pipelines. * Familiarity with MLPerf or other large-scale benchmarking frameworks. * Contributions to OSS projects such as llm-d, vLLM or PyTorch. * Exposure to benchmarking GPU clusters or multi-region environments. * Background working with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies. ## Description We're looking for a Senior Engineer for CoreWeave's Benchmarking & Performance team. You will have an integral part in 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 aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf. You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across multiple services. You'll partner with product, orchestration, and hardware teams to evolve our Kubernetes-native platform and meet strict P99 SLAs at scale. What you'll do * Develop and enhance Kubernetes-native benchmarking services that measure latency, throughput, jitter, and cost-per-request across CoreWeave's compute stack. * Contribute to implementing and maintaining benchmarking workflows for end-to-end MLPerf Training and Inference runs, including workload setup, cluster configuration, and result validation. * Participate in design discussions and contribute to architecture decisions within the team. * Break down engineering tasks into clear milestones and deliver reliable, high-quality code. * Collaborate with teammates to maintain reproducible, well-documented benchmarking processes. * Provide constructive code reviews and share best practices with peers. * Mentor junior engineers; review cross-team designs and elevate coding/testing standards. * Help ensure reproducible, well-documented benchmarking processes. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Accelerating Python on GPUs](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [All your telemetry data from any source in one place](https://www.wearedevelopers.com/videos/57-all-your-telemetry-data-from-any-source-in-one-place) ## Related Articles - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Dev Digest 103 - Superb Owl Trafficking](https://www.wearedevelopers.com/magazine/388-dev-digest-103-superb-owl-trafficking) - [Why Attend a Developer Event in 2026?](https://www.wearedevelopers.com/magazine/688-why-attend-a-developer-event-in-2026)