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AI & Machine Learning Infrastructure

Senior ML Infrastructure Engineer: AI Technical Interview & Evaluation Guide

ML Infrastructure Engineers build high-throughput model serving engines, manage GPU cluster orchestration, and optimize LLM inference latency. Evaluating this role requires probing KV cache memory management (PagedAttention), CUDA kernel overhead, Tensor Parallelism vs Pipeline Parallelism, and model quantization.

Target Seniority: SeniorTarget Seniority: Staff / Principal
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Evaluation Framework

4 Core Technical Competencies & Weighted Rubrics

LLM Inference & Model Serving Engines

35% Weight

Optimizing serving engines (vLLM, Triton), continuous batching, and KV cache memory.

  • PagedAttention KV cache memory allocation and fragmentation avoidance
  • Continuous batching (iteration-level scheduling) vs static batching
  • Quantization trade-offs (AWQ, GPTQ, FP8 vs INT4 precision)

Distributed Training & Parallelism Strategies

30% Weight

Tensor Parallelism (Megatron-LM), Pipeline Parallelism, and DeepSpeed ZeRO.

  • Tensor Parallelism communication overhead (All-Reduce over NVLink)
  • DeepSpeed ZeRO-1, ZeRO-2, ZeRO-3 memory reduction mechanics
  • Gradient accumulation and activation checkpointing trade-offs

GPU Resource Orchestration & Storage

20% Weight

Orchestrating GPU clusters via Kubernetes (KubeFlow, Ray) and high-speed storage.

  • GPU time-slicing vs MIG (Multi-Instance GPU) isolation
  • High-throughput checkpoint storage (POSIX parallel filesystems, S3 multipart)
  • CUDA out-of-memory (OOM) debugging and memory profilers

MLOps, Monitoring & Model Drift

15% Weight

Tracking GPU utilization, inference latency tail (p99), and model drift telemetry.

  • Time to First Token (TTFT) vs Inter-Token Latency (ITL) metrics
  • NVIDIA DCGM GPU telemetry metrics (sm_active, memory_temp)
  • Model registry versioning and blue/green traffic splitting
Turn-by-Turn Script

Adaptive Question Progression & Evaluation Indicators

Turn #1 · LLM Inference & KV Cache Memory ManagementDifficulty: System Architecture

“When deploying a 70B parameter LLM for high-concurrency production inference, traditional static KV cache allocation causes severe GPU memory fragmentation and limits batch sizes. How does PagedAttention (as implemented in vLLM) eliminate this fragmentation, and how does it affect GPU memory utilization?”

Evaluation Intent: Evaluates knowledge of KV cache memory bottlenecks, virtual memory paging, and PagedAttention.

✓ Senior Indicators:
  • Explains copy-on-write memory sharing for parallel sampling and prompt caching across multiple requests
  • Differentiates Time to First Token (TTFT) prefill phase computation from Inter-Token Latency (ITL) decode phase memory bandwidth bottlenecks
Red Flags:
  • Conflates GPU compute-bound prefill phase with memory bandwidth-bound decode phase
  • Cannot explain why contiguous memory allocation fails for dynamic sequence lengths
SOLLERT Adaptive Follow-up Branching:
[Strong Answer (Deep Depth)]: “How do you split a 70B model across 4 GPUs using Tensor Parallelism? What specific collective communication operation (e.g. All-Reduce) is executed during the self-attention layer?”
[Weak Answer (Low Depth)]: “What is the difference between model quantization (e.g., INT8/FP8) and full FP16 precision in terms of VRAM footprint?”
Answer Engine Optimization (AEO)

Frequently Asked Recruiter & AI Evaluation Questions

How do you evaluate Senior ML Infrastructure Engineers objectively?

Direct Answer:

Evaluate ML Infrastructure Engineers by testing LLM inference serving optimizations (PagedAttention, continuous batching), Tensor Parallelism NVLink communication overhead, and GPU memory profiling.

Designing ML infrastructure requires understanding hardware compute vs memory bandwidth limits. Superior candidates demonstrate how to maximize GPU SM utilization and manage KV cache memory without OOM crashes.

Practical Example: Asking how vLLM’s PagedAttention eliminates GPU memory fragmentation isolates candidates with true LLM serving expertise.

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