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# Software Engineer, GPU Kernels

- **Company:** River AI
- **Location:** Palo Alto, CA, United States
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, C++ (Programming Language), Profiling, Nvidia CUDA, Computer Engineering, Software Debugging, Python (Programming Language), Graphics Processing Unit (GPU), Pytorch, Numerical Computing, Information Technology, SGLang, Free and Open-Source Software, VLLM, CUTLASS
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_72d56ba4ab162888a42d2cd37297153c

## About the Role

* Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
 * Experience optimizing GPU kernels with CUDA, Triton, CUTLASS, CuTe, or comparable tools.
 * Strong understanding of GPU architecture, memory hierarchies, and parallel execution.
 * Proficiency in C++ and Python.
 * Strong foundations in linear algebra, floating-point arithmetic, and numerical computing.
 * Strong debugging and profiling skills, with a collaborative approach to engineering., (We encourage you to apply even if you don't meet all of these)
 * Experience optimizing for NVIDIA Blackwell or Hopper GPUs.
 * Work on attention, mixture-of-experts kernels, grouped GEMMs, or low-rank adapters.
 * Experience implementing backward passes and validating gradients.
 * Familiarity with FP8, FP4, and quantized weight layouts.
 * Experience integrating custom operators into PyTorch, SGLang, vLLM, or similar frameworks.
 * Open-source contributions or a track record of shipping substantial kernel optimizations.

## Description

We are looking for exceptional GPU kernel engineers to build the compute primitives behind River's training and inference infrastructure. Your goal is to make large models faster to train and more efficient to serve.

You will own performance-critical operations, including attention, matrix multiplication, mixture-of-experts execution, and low-precision computation. Working closely with researchers and systems engineers, you will identify bottlenecks, implement kernels, validate correctness, and bring improvements into production.

What You'll Do

 * Build fast GPU kernels for attention, matrix multiplication, expert routing, and related operations.
 * Optimize memory access, tiling, and synchronization to make efficient use of GPU hardware.
 * Develop FP8, FP4, and mixed-precision kernels while preserving numerical correctness.
 * Accelerate fine-tuning and RL through optimized adapters, backward passes, and fused operations.
 * Profile real workloads and integrate improvements into training and inference runtimes.
 * Build reproducible benchmarks that verify correctness, gradients, and performance.