Workload
0–10
Quantify model, hardware, request or job shape, SLO, utilization, and cost objective.
Evidence: A capacity model with the dominant constraint.
Who this is for
Software, distributed-systems, ML platform, performance, and infrastructure engineers preparing for AI companies or teams that operate accelerator-heavy workloads.
Use the mock interview deliberately
What this practice should reveal
AI infrastructure interviews combine conventional distributed-systems reasoning with unusual resource shapes. GPUs are expensive, jobs are long-running, artifacts are large, communication can dominate compute, and an optimization that improves throughput may damage latency or model quality.
Start with the workload and objective: training or inference, batch or interactive, model shape, hardware, latency or completion target, reliability, isolation, and cost. Only then choose scheduling, parallelism, batching, caching, checkpointing, and placement strategies.
Core capabilities
What to practice under pressure
1
Workload characterization
Estimate model, memory, compute, communication, request shape, batch behavior, latency, throughput, and utilization targets.
2
Scheduling and isolation
Handle topology, gang scheduling, quotas, priorities, preemption, fragmentation, noisy neighbors, and fair sharing.
3
Training and serving paths
Design parallelism, checkpoints, artifact movement, model loading, batching, routing, caching, and autoscaling.
4
Performance and reliability
Use traces and profiles to find bottlenecks; recover from worker, network, storage, and accelerator failure without losing control of cost.
Timed session
A 60-minute mock interview plan
PhaseTimeActivityEvidence to produce
Core system
10–30
Design scheduler, execution, storage, networking, routing, and control-plane responsibilities.
Evidence: Clear resource and artifact lifecycles.
Optimize
30–46
Evaluate parallelism, placement, batching, caching, memory, and communication trade-offs.
Evidence: A bottleneck-driven optimization order.
Recover
46–60
Handle failures, checkpoints, degraded capacity, observability, admission, and cost anomalies.
Evidence: Recovery objectives and operator actions.
Live practice
Run these interview scenarios
The scenarios are independent practice. Open the public prompt first, then run it in the matching workspace with voice follow-ups and evidence-based review.
Hard · system design
Design a Multi-Tenant LLM Inference Platform
Design a global inference service for mixed chat, coding, and long-context workloads across heterogeneous accelerators.
Hard · system design
Design a Fault-Tolerant Distributed LLM Training Platform
Design the training stack for a 100B+ parameter model across tens of thousands of accelerators.
Hard · system design
Design a Shared GPU Cluster Scheduler
Design scheduling and capacity management for training, research, and production inference on a large heterogeneous accelerator fleet.
Hard · system design
Design an LLM Inference Performance Optimization Program
Design the measurement, kernel, rollout, and correctness system that closes the gap between theoretical and achieved inference throughput.
Self-review scorecard
Look for evidence, not confidence
Capacity model
Strong signal: Identifies the dominant compute, memory, network, or latency constraint.
Warning signal: Scales GPU count without estimating the workload.
Resource design
Strong signal: Accounts for topology, placement, fragmentation, and isolation.
Warning signal: Treats accelerators as interchangeable stateless workers.
Optimization
Strong signal: Measures first and orders changes by bottleneck.
Warning signal: Lists optimizations without interaction or quality costs.
Reliability
Strong signal: Defines checkpoints, retries, admission, and degradation.
Warning signal: Restarts an expensive job from zero after any failure.
Frequently asked questions
AI Infrastructure Interview Questions FAQ
What topics appear in AI infrastructure interviews?
Common areas include distributed training, accelerator scheduling, model serving, batching, caching, parallelism, artifact storage, performance profiling, fault recovery, observability, and cost.
Do I need deep ML theory for an AI infrastructure role?
You need enough model understanding to reason about workload shape, memory, compute, communication, batching, quality constraints, and evaluation, while systems depth remains central.
How should I approach a GPU scheduler design?
Start with job shapes and policy goals, then cover topology, gang scheduling, queues, quotas, priorities, preemption, fragmentation, failure recovery, utilization, and fairness.