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Metro location and mid-level role with known brand, but niche ML-hardware test skills moderate competition.
Role requires niche ML-inference, C++, and accelerator validation skills, limiting cross-industry transferability.
Mandatory C++, strong OOP, ML operator knowledge, numerical validation and explicit years make screening strict.
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Validate neural network operators and libraries for correctness and performance on AI computational storage hardware.
Develop and maintain automated test suites and benchmarking frameworks for neural network operators and graphs.
Analyze software performance, debug across execution stacks, and collaborate with compiler, runtime, and hardware teams.
4–6 years of experience in AI/ML systems validation or performance testing.
Strong C++ programming skills and Object-Oriented Programming fundamentals (mandatory).
Experience with Python for test automation.
Strong understanding of deep learning operators and frameworks, preferably PyTorch.
Experienced in numerical accuracy validation and floating-point behavior related to deep learning operators.
Familiar with performance profiling and debugging tools like perf, VTune, and Nsight.
Knowledgeable about ML execution stacks (e.g., MLIR/XLA) and hardware accelerator validation (GPU/NPU/FPGA).