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# GPU Kernel Engineer

- **Company:** FriendliAI Corp.
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Nvidia CUDA, Computer Engineering, Performance Tuning, Graphics Processing Unit (GPU), Gpu Programming, Numerical Computing, Information Technology, Low Latency, Free and Open-Source Software, ROCm, Model Inference, CUTLASS
- **Published:** October 14, 2026
- **Apply:** https://startup.jobs/software-engineer-gpu-kernel-friendliai-10450640

## About the Role

* 3+ years of experience in GPU programming, HPC, or performance-critical systems
 * Bachelor's or Master's degrees in Computer Science, Computer Engineering, Electrical Engineering, or a related field
 * Strong proficiency in CUDA for NVIDIA GPUs or ROCm/HIP for AMD GPUs
 * Deep understanding of GPU architecture: warps, threads, memory hierarchy, synchronization, and latency-throughput trade-offs
 * Proficiency in C++
 * Experience with GPU profiling and performance tuning
 * Strong numerical background with understanding of precision trade-offs and quantization techniques, * Experience optimizing transformer, multi-modal, or Mixture-of-Experts (MoE) architectures at the kernel level
 * Familiarity with the latest GPU libraries and frameworks (CUTLASS, Triton, …)
 * Inter-GPU communication programming experience
 * Open-source contributions related to GPU performance or ML acceleration
 * Research or conference presentations on GPU optimization, HPC, or numerical computing

## Description

* Design, implement, and optimize high-performance GPU kernels for AI inference (e.g., GEMM, attention, routing)
 * Develop and maintain GPU code in CUDA and C++, including low-level assembly when needed
 * Implement reduced-precision and quantized kernels (FP8/FP4) for low-latency or high-throughput inference
 * Benchmark and ensure cross-vendor performance parity between NVIDIA and AMD hardware
 * Contribute to internal GPU libraries and tune performance of performance-critical components
 * Accelerate multi-modal model pipelines
 * Investigate and integrate next-generation GPU features