- Design and optimize ML kernels targeting GPUs (CUDA), focusing on throughput latency memory efficiency profiling benchmarking analysis bottleneck identification numerical format reasoning low-level debug support collaboration with internal teams expertise sharing exploration prototyping alternative platforms tradeoff advice best practices kernel implementation.
- Strong experience in writing optimizing CUDA or equivalent code deep understanding GPU architecture parallel execution memory hierarchy knowledge C++ programming proficiency independently investigate ambiguous poorly understood issues drive resolution comfortable switching different ecosystems learning new stacks ability fast troubleshooting debugging tools Nsight Systems Compute nvprof framework profilers.
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Member of Technical Staff, ML Kernels - Santa Clara - Netpreme
Description
About the Role
We're seeking a Member of Technical Staff, Machine Learning Kernels to design and optimize high-performance compute kernels for modern machine learning workloads. This role is for a deeply technical engineer who enjoys working close to hardware – writing CUDA kernels, investigating subtle performance artifacts, building benchmarks and serving as an expert on accelerator behavior.
As a hands-on performance specialist, you'll partner closely with research, systems and infrastructure teams to unlock efficiency gains across GPUs today and other accelerators (e.g., TPU) as we expand our hardware partnerships.
Responsibilities:
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