GPU Optimization Engineer

16 hours ago

bangalore, India Taglynk Full time

Role We’re hiring a GPU Optimization Engineer who understands GPUs at a deep, architectural level — someone who knows exactly how to squeeze every last millisecond out of a model, what GPU constraints matter, and how to restructure models for real-world inference performance. You’ll work across CUDA kernels, model graph optimizations, hardware-specific tuning, and porting models across GPU architectures. Your work directly impacts the latency, throughput, and reliability of smallest’s real-time speech models. What You’ll Do Optimize model architectures (ASR, TTS, SLMs) for maximum performance on specific GPU hardware Profile models end-to-end to identify GPU bottlenecks — memory bandwidth, kernel launch overhead, fusion opportunities, quantization constraints Design and implement custom kernels (CUDA/Triton/Tinygrad) for performance-critical model sections Perform operator fusion, graph optimization, and kernel-level scheduling improvements Tune models to fit GPU memory limits while maintaining quality Benchmark and calibrate inference across NVIDIA, AMD, and potentially emerging accelerators Port models across GPU chipsets (NVIDIA → AMD / edge GPUs / new compute backends) Work with TensorRT, ONNX Runtime, and custom runtimes for deployment Partner with the research and infra teams to ensure the entire stack is optimized for real-time workloads Requirements Strong understanding of GPU architecture — SMs, warps, memory hierarchy, occupancy tuning Hands-on experience with CUDA , kernel writing, and kernel-level debugging Experience with kernel fusion and model graph optimizations Familiarity with TensorRT, ONNX, Triton, tinygrad, or similar inference engines Strong proficiency in PyTorch and Python Deep understanding of model architectures (transformers, convs, RNNs, attention, diffusion blocks) Experience profiling GPU workloads using Nsight, nvprof, or similar tools Strong problem-solving abilities with a performance-first mindset Great to Have Experience with quantization (INT8, FP8, hybrid formats) Experience with audio/speech models (ASR, TTS, SSL, vocoders) Contributions to open-source GPU stacks or inference runtimes Published work related to systems-level model optimization Who Will Succeed in This Role Someone who: thinks in kernels, not just layers knows which optimizations are theoretical vs practically impactful understands GPU boundaries (memory, bandwidth, latency) and how to work around them is excited by the challenge of ultra-low latency and large-scale real-time inference loves debugging at the CUDA + model level

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