Technical practice
Designing Custom CUDA Kernels for Neural Network Operators
What to coverDiscuss how you approach writing custom CUDA kernels for performance-critical neural network operators. Candidates should address memory coalescing, shared memory usage, register pressure, and warp divergence management to ensure maximum throughput on custom hardware.
Implementing Operator Fusion Strategies
What to coverExplain the mechanics of operator fusion, such as combining matrix multiplication, bias addition, and LayerNorm into a single kernel. Discuss how fusion reduces global memory traffic and improves hardware execution efficiency.
Managing Memory Hierarchy and Data Movement
What to coverHow do you optimize data movement across various levels of the memory hierarchy in an AI accelerator? Focus on latency hiding techniques, asynchronous data transfers, and maximizing on-chip memory utilization.
Runtime-to-NN Layer Interface Design
What to coverDescribe how you design robust execution models and clean interfaces between the runtime system and the neural network layer. Address command queue management, stream synchronization, and hardware scheduling.
Integrating with Deep Learning Frameworks
What to coverHow do you connect custom runtime APIs and operator libraries with high-level frameworks like PyTorch, TensorFlow, or JAX? Discuss custom C++ extensions, ATen dispatch mechanisms, and tensor layout conversions.
Compiler and Runtime Interaction (XLA / MLIR)
What to coverExplain how graph-level compilers like XLA or MLIR interact with execution runtimes during operator lowering and optimization. Address how intermediate representations inform hardware-specific code generation.
Debugging and Resolving Performance Bottlenecks
What to coverWalk through your methodology for identifying and resolving performance bottlenecks across the hardware/software stack. Mention profiling tools, hardware performance counters, and analyzing compute versus memory-bound workloads.
Optimizing Large-Scale DL and LLM Workloads
What to coverWhat specific challenges arise when optimizing runtimes and operators for large-scale deep learning models or LLMs, such as large KV caches or dynamic batch sizes? Discuss memory footprint reduction and pipeline parallel execution strategies.
Behavioural practice
Cross-Functional Collaboration with Compilers and Framework Teams
What to coverDescribe a situation where you had to collaborate closely with separate compiler, framework, or hardware teams to deliver a complex feature. Use the situation/action/result format to explain how you aligned technical priorities and resolved integration blockers.
Resolving a Critical Performance Bottleneck Under Pressure
What to coverTell me about a time when you diagnosed and resolved a severe performance bottleneck in a systems software or runtime layer. Outline your investigative steps, how you verified the fix, and the measurable impact on system efficiency using the STAR method.
Balancing Ambiguity in Custom Hardware Development
What to coverProvide an example of working on a next-generation hardware platform or early-stage architecture where specifications were evolving. Explain how you maintained momentum, drove technical decisions, and managed risks.
Mentoring and Driving Technical Ownership
What to coverDescribe a project where you took ownership of a core subsystem or runtime layer and guided engineering quality. Share how you communicated architectural trade-offs to stakeholders and supported team execution.