Technical practice
Validating Neural Network Operators for Correctness
What to coverDiscuss how you validate complex neural network operators such as matrix multiplication, convolutions, or attention mechanisms against reference models. Address how you handle floating-point precision differences and numerical validation between custom kernels and frameworks like PyTorch or TensorFlow.
C++ Object-Oriented Principles in Test Frameworks
What to coverExplain how you apply strong C++ and Object-Oriented Programming principles when building scalable and maintainable automated test frameworks. Discuss design patterns or class hierarchies you use to abstract operator-level and graph-level testing.
Python for Test Automation and Scripting
What to coverDescribe how you leverage Python to build robust test automation suites that drive execution, capture logs, and verify outputs against reference frameworks. Highlight how you interface Python scripts with underlying C++ libraries or execution engines.
Numerical Accuracy and Floating-Point Behavior
What to coverExplain the challenges of floating-point rounding, accumulation errors, and precision loss (e.g., FP32 vs. FP16/BF16) when validating neural network libraries. Discuss how you establish acceptable numerical tolerance thresholds.
Analyzing Execution Bottlenecks via Software Profiling
What to coverDiscuss your experience with software profiling tools (such as perf, VTune, or Nsight) to analyze performance bottlenecks in compute kernels. Explain how you measure throughput, latency, and hardware utilization.
Memory Usage Analysis and Leak Detection
What to coverExplain your methodology for conducting memory usage analysis and detecting memory leaks within high-performance compute libraries or hardware execution stacks. Mention tools or techniques you rely on for memory tracking.
Debugging Across the Compiler-to-Hardware Stack
What to coverWalk through a systematic approach to debugging a failure that spans the compiler, runtime APIs, and hardware execution stack. Explain how you isolate whether a bug originates in graph compilation, runtime dispatch, or kernel implementation.
Benchmarking Throughput, Latency, and Scaling Behavior
What to coverDescribe how you design and maintain performance benchmarking frameworks to evaluate scaling behavior across diverse hardware platforms. Address how you ensure consistent and repeatable test conditions.
Validating ML Execution Stacks and Accelerators
What to coverDiscuss your familiarity with ML execution stacks (such as MLIR/XLA) or validating workloads on specialized accelerators like GPUs, NPUs, or FPGAs. Explain how hardware memory hierarchies and parallel execution impact your test strategy.
Behavioural practice
Collaborating Across Compiler, Runtime, and Hardware Teams
What to coverDescribe a situation where you had to collaborate closely with cross-functional engineering teams (such as compiler, runtime, or hardware design) to resolve a complex bug or performance bottleneck. Use the STAR method to explain your communication strategy, how you shared profiling data, and the final outcome.
Handling Ambiguity in Deep Learning System Validation
What to coverShare an instance where you faced ambiguous requirements or an undocumented failure mode while testing a new deep learning operator or library. Detail how you structured your investigation, formed hypotheses, and successfully isolated the root cause.
Prioritizing Test Coverage Under Tight Deadlines
What to coverProvide an example of a time when you had to balance comprehensive smoke and regression test execution with urgent release timelines. Explain how you prioritized test cases, automated repetitive tasks, and maintained quality standards.
Driving Continuous Improvement in Test Automation Frameworks
What to coverDescribe a project where you identified inefficiencies or limitations in an existing performance benchmarking or test automation framework. Outline the actions you took to redesign or scale the framework and the measurable impact on the team's productivity.