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Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Microsoft AI Infra interview where the whole session was a live vibe-coding exercise in CUDA. You bring your own editor with an AI assistant set up and they watch how you prompt, critique, and fix the generated kernel code. Pretty different format from the usual whiteboard grind.

Questions Asked (1)

Q1

Using an AI coding assistant in your editor, implement and tune a CUDA kernel (such as tiled matrix multiplication, a fused attention kernel, or a reduction). Walk through your prompting strategy, critique the AI's output, and fix any issues you find.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This was the whole interview basically.

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AI HintsAI Generated

Suggested Approach

Demonstrate a structured workflow: start by clarifying the kernel's requirements and constraints, then craft precise prompts to the AI assistant, critically evaluate the generated code for correctness and performance, and iteratively refine through testing and profiling. Emphasize that AI is a tool to accelerate development, not a replacement for deep understanding of CUDA and parallel computing.

Pro tip: Always verify AI-generated CUDA code with a reference implementation and use profiling tools like Nsight Compute to identify bottlenecks; AI often misses subtle issues like bank conflicts or insufficient occupancy.

1. Clarify Requirements and Constraints

Define the kernel's purpose, input/output shapes, performance targets, and hardware constraints (e.g., GPU architecture, memory limits). This guides both your prompting and evaluation.

2. Craft Effective Prompts

Write specific prompts that include the kernel type, desired optimizations (e.g., tiling, shared memory usage), and any constraints. Ask the AI to explain its reasoning and provide multiple versions if possible.

3. Critically Evaluate AI Output

Review the generated code for correctness (e.g., race conditions, boundary handling), performance (e.g., memory access patterns, occupancy), and adherence to CUDA best practices. Compare with known optimal implementations.

4. Test and Profile

Compile and run the kernel with test cases, validate against a reference, and profile using tools like Nsight Compute or nvprof to identify bottlenecks and measure performance.

5. Iterate and Refine

Based on profiling results, manually optimize or prompt the AI for specific improvements (e.g., reduce shared memory bank conflicts, increase ILP). Repeat until performance goals are met.

Key Points to Mention

  • Importance of understanding CUDA fundamentals (thread hierarchy, memory model, synchronization) to evaluate AI output.
  • Prompt engineering techniques: providing context, specifying constraints, asking for explanations, and requesting multiple solutions.
  • Common pitfalls in AI-generated CUDA code: incorrect thread indexing, missing __syncthreads(), uncoalesced memory accesses, and suboptimal tile sizes.
  • Performance analysis: using profiling tools to measure achieved occupancy, memory throughput, and identify bottlenecks.
  • Trade-offs between different optimization strategies (e.g., larger tiles vs. register pressure, shared memory vs. L1 cache).
  • The role of AI as an accelerator, not a replacement for expertise; always validate and understand the code.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.