Senior Software Engineer - GPU Kernel Authoring & Optimization

CoreWeave•Sunnyvale, CA

2d•$182,000 - $242,000•Onsite

About The Position

CoreWeave is seeking a Senior Engineer for its Benchmarking & Performance team, focusing on kernel authoring and optimization. This role involves writing, profiling, and tuning GPU kernels critical for large-scale model serving, aiming to maximize throughput and minimize latency. The engineer will also contribute to industry-leading performance benchmarking publications like MLPerf. The position requires ownership of designs, raising engineering standards, and delivering measurable improvements in latency, throughput, and reliability across the inference stack. Collaboration with product, orchestration, and hardware teams is essential to translate kernel-level optimizations into end-to-end gains and meet strict P99 SLAs at scale.

Requirements

5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.
Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.
Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.
Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.
Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.
Strong communicator comfortable collaborating with cross-functional teams and external partners.

Nice To Haves

Triton or Mojo for authoring custom GPU kernels — highly desired.
CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.
JAX and its Pallas kernel language for authoring kernels on GPU/TPU.
HIP / ROCm and AMD GPU experience.
NCCL and collective-communication performance.
Experience with alternative accelerators such as Google TPUs and Meta's MTIA.
Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.
Experience with Kubernetes at production scale.
Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.
Experience running MLPerf submissions or similar large-scale audited benchmarks.
Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.

Responsibilities

Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.
Optimize for hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.
Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.
Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).
Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.
Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.
Mentor junior engineers; review cross-team designs and elevate coding/testing standards.
Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.