Sr. SW Test Engineer - Neural Library (5-8 years)
Summary
The Senior Software Test Engineer will validate neural network operators and libraries for AI computational storage products using C++ and Python. The role involves performance benchmarking, numerical accuracy validation, and debugging across the compiler, runtime, and hardware stack.
We are looking for a Sr. SW Test Engineer - Neural Library to validate neural network operators and libraries for a next-generation AI computational storage product. You will ensure correctness, performance, and robustness of compute kernels deployed on advanced hardware platforms.
Key Responsibilities
Validate neural network operators (matmul, convolution, attention, etc.) for correctness using reference models
Develop automated tests for operator-level and graph-level validation
Perform numerical accuracy validation against frameworks like PyTorch or TensorFlow
Execute and maintain smoke and regression test suites
Benchmark and validate performance (throughput, latency, scaling behavior)
Build and maintain performance benchmarking frameworks
Perform software profiling to analyze execution bottlenecks
Conduct memory usage analysis and detect memory leaks
Identify issues across compiler → runtime → kernel execution stack
Debug failures across compute kernels, runtime APIs, and hardware execution
Collaborate with compiler, runtime, and hardware teams
Required Qualifications
4–6 years in AI/ML systems validation or performance testing
Strong C++ programming skills (mandatory)
Strong Object-Oriented Programming (OOP) fundamentals (mandatory)
Experience with Python for test automation
Strong understanding of deep learning operators and frameworks (PyTorch preferred)
Experience with numerical validation and floating-point behavior
Knowledge of parallel execution, memory hierarchy, and compute kernels
Preferred
Experience with profiling and debugging tools (perf, VTune, Nsight, etc.)
Experience with ML execution stacks (MLIR/XLA or similar)
Exposure to accelerator validation (GPU/NPU/FPGA)
Experience validating large language model workloads
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