Technical practice
How do you validate numerical correctness of a custom neural network operator against a PyTorch or TensorFlow reference model?
What to coverSuggested practice question based on the role requirement for numerical validation and operator testing. A strong response should detail selecting absolute and relative tolerance thresholds (atol/rtol) based on data types (FP32 vs FP16/BF16), handling numerical drift from different accumulation orders, and validating corner cases like NaNs, infs, and zero tensors.
How would you architect an automated C++ and Python testing framework for operator-level and graph-level regression testing?
What to coverSuggested practice question based on the stated responsibilities for automated tests, C++, and Python. The candidate should discuss clean OOP design in C++ for execution drivers, Python bindings or scripting for test orchestration and parameterized fixtures, and integrating continuous smoke and regression test suites.
When a compute kernel produces non-deterministic numerical discrepancies, how do you debug and isolate the issue?
What to coverSuggested practice question derived from the requirement to debug failures across compute kernels, runtime APIs, and hardware execution. The candidate should explain systematic fault isolation: checking thread race conditions, parallel reduction order, shared memory bounds, floating-point precision modes, and verifying against CPU reference runs.
How do you measure and benchmark latency, throughput, and scaling behavior for neural network compute kernels?
What to coverSuggested practice question based on the performance benchmarking framework responsibilities. The candidate should address warm-up runs, timing overhead, cache flushing to prevent artificial hits, batch size scaling, and calculating roofline performance relative to peak theoretical hardware bandwidth and compute limits.
What profiling strategies and tools (such as perf, VTune, or Nsight) would you use to detect execution bottlenecks in an AI compute kernel?
What to coverSuggested practice question based on preferred profiling tools and execution bottleneck analysis duties. The response should cover identifying compute-bound versus memory-bandwidth-bound operations, analyzing cache miss rates and memory access strides, and interpreting instruction pipeline stalls.
How do you isolate whether a bug originates in the compiler transformation, runtime layer, or underlying compute kernel?
What to coverSuggested practice question based on the core duty to identify issues across the compiler -> runtime -> kernel stack. The candidate should describe inspecting intermediate representations (IR), dumping generated kernel code, validating raw runtime API payloads independently, and executing kernels directly with fixed inputs to bypass compiler optimizations.
How do you detect memory leaks and evaluate memory hierarchy utilization during sustained execution of a neural library?
What to coverSuggested practice question reflecting the responsibility for memory usage analysis and leak detection. The candidate should address using tools such as Valgrind or sanitizers (ASan/LSan), monitoring resident memory across long regression runs, and verifying alignment and buffer reuse across memory tiers.
What specific considerations and test strategies apply when validating large language model (LLM) workloads compared to standard CNNs?
What to coverSuggested practice question based on the preferred qualification in LLM workload validation. The candidate should discuss testing multi-head attention kernels, managing dynamic sequence lengths, key-value (KV) cache memory overhead, and numerical sensitivity in low-precision (INT8/FP8/FP16) matrix multiplications.
How do parallel execution models and hardware memory hierarchies impact kernel test design on accelerators like NPUs or GPUs?
What to coverSuggested practice question based on the required knowledge of parallel execution and accelerator validation. The candidate should discuss designing tests that stress warp/thread divergence, shared memory bank conflicts, memory coalescing, and synchronization primitives across parallel execution units.
Behavioural practice
Describe a scenario where you uncovered a critical failure that spanned across compiler, runtime, and hardware boundaries.
What to coverSuggested practice question aligned with cross-functional debugging duties. The candidate should use the STAR method to describe how they systematically isolated the defect, communicated diagnostic findings to compiler or hardware teams, and drove the resolution.
Tell me about a time you had to resolve a disagreement with development or hardware teams over whether an anomaly was a bug or expected behavior.
What to coverSuggested practice question addressing collaboration with compiler and hardware teams on numerical tolerances. The candidate should highlight their approach to presenting objective empirical data, assessing system-level impact, and reaching a consensus on acceptable precision or specification trade-offs.
Give an example of how you prioritized validation efforts and maintained regression test coverage under tight delivery schedules.
What to coverSuggested practice question based on maintaining smoke and regression suites in a senior role. The candidate should explain how they assessed risk to balance quick-turnaround smoke tests with comprehensive numerical sweeps and automated nightly regression pipelines.
Describe a situation where you identified a severe performance bottleneck or memory leak before code reached production.
What to coverSuggested practice question focused on profiling and memory analysis responsibilities. The candidate should detail the diagnostic tools they deployed, the root cause discovered in the compute kernel or runtime, and the measurable performance recovery achieved.