Assess an inference engineer by how they characterize a workload, locate bottlenecks, and test performance changes without losing sight of output quality. Naming optimization techniques is less useful than showing when and why to use them.
Use a fictional performance investigation with synthetic measurements. Do not request access to a live production service.
Describe the workload before the problem
Provide the type of model, request shape, concurrency pattern, hardware context, and user expectations at a general level. Explain whether the workload is interactive or batch-oriented.
Ask what information is missing. A candidate should distinguish request latency, throughput, queueing, resource use, and the quality constraints that must remain acceptable.
Give a bounded investigation
The fictional service becomes slower during bursts while average resource utilization appears moderate. Ask the candidate to propose an investigation sequence and a controlled experiment.
A useful answer should explain which observations would distinguish queueing, preprocessing, model execution, transfer, or application overhead.
Discuss optimization as a tradeoff
NVIDIA’s Triton documentation describes dynamic batching as combining requests and exposes queue-delay controls. This makes batching a useful example of a workload-dependent performance decision, not an automatic improvement for every service. NVIDIA Triton batching documentation.
Ask what the candidate would measure before and after changing batching or concurrency. Avoid requiring memorized configuration syntax unless the job truly depends on it.
Score the reasoning
| Dimension | Evidence |
|---|---|
| Workload understanding | Identifies the relevant request and traffic characteristics |
| Diagnosis | Separates possible bottlenecks with useful measurements |
| Experiment design | Changes variables deliberately and checks repeatability |
| Quality | Tests whether performance changes alter acceptable outputs |
| Operation | Plans rollout, monitoring, and rollback |
| Communication | Explains the tradeoff in terms of user needs |
Test skepticism
Present a fictional benchmark that looks better but uses different request lengths or hardware. Ask whether the comparison supports the claim.
The candidate should notice differences that could explain the result and propose a more informative comparison.
Use the AI infrastructure mandate and research evidence review. Discuss an AI infrastructure hire with Refery.