CUDA Kernel Engineer
# CUDA Kernel Engineer **Pragmatike** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Mid-level` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[GPU-Engineer]` `[CUDA-Kernel-Engineering]` `[High-Performance-Computing-Engineer]` `[GPU-Software-Engineer]` `[AI-Infrastructure-Engineer]` `[CUDA-Engineer]` `[CUDA-Software-Engineer]` `[Compute-Kernels-Engineer]` `[CUDA-Software-Engineering]` `[CUDA-Developer]` `[Kernel-Engineer]` --- ### About the Role Location: Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital , founded by MIT CSAIL researchers. We are searching for a CUDA Kernel Engineer who has hands-on experience developing and optimizing NVIDIA CUDA kernels from scratch . You will work on the GPU performance layer powering large-scale, high-throughput AI systems used by Fortune 500 customers. This role is ideal for someone who deeply understands NVIDIA GPU architecture, memory hierarchy, warp-level execution, and profiling workflows not someone coming from generic hardware, FPGA, or non-NVIDIA compute backgrounds. You will directly influence the GPU efficiency, throughput, and scalability of mission-critical AI systems. What Youll Do - Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs , with a focus on maximizing occupancy, memory throughput, and warp efficiency. - Profile GPU workloads using tools such as N sight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK . - Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead. - Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing. - Collaborate closely with AI systems, model acceleration, and backend distributed systems teams. - Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices. What Were Looking For - Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries. - Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling). - Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it) - Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap). - Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling. Bonus Points - Experience with multi-GPU or distributed GPU systems (NCCL, NVLink, MIG). - Background in GPU acceleration for ML frameworks or HPC workloads. - Knowledge of model inference optimization (TensorRT, CUDA Graphs, CUTLASS). - Exposure to compiler-level optimization or PTX/SASS analysis. - Startup experience or comfort working in fast-moving, ambiguous environments. Why This Role Will Pivot Your Career - Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions. - Customer impact: Deploy AI solutions powering Fortune 500 clients . - Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave). - Funding & growth: Oversubscribed seed round, next funding in 2026. - Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale . - Culture & autonomy: Own critical systems while collaborating with world-class engineers. - Aspirational impact: Solve GPU/AI performance challenges few engineers ever face. Benefits - Competitive salary & equity options - Sign-on bonus - Health, Dental, and Vision - 401k Originally posted on Himalayas
- GPU-Engineer
- CUDA-Kernel-Engineering
- High-Performance-Computing-Engineer
- GPU-Software-Engineer
- AI-Infrastructure-Engineer
- CUDA-Engineer
- CUDA-Software-Engineer
- Compute-Kernels-Engineer
- CUDA-Software-Engineering
- CUDA-Developer
- Kernel-Engineer
Наблюдалась 2026-09-30, впервые 2026-09-30, источник — Himalayas (JSON API).