PREP FOR YOUR TARGET COMPANY
Interview prep by company
Compare role-specific study priorities, public interview information and relevant practice. Each page marks what we could verify and where the evidence is limited. Companies are grouped by the kind of infrastructure work they do.
47 companies·21 core AI infra employers·updated for 2026
Frontier model labs
7Model training and serving create preparation topics in accelerator efficiency, distributed execution and recovery. Confirm the target team’s responsibilities.PREPARATION FOCUS: Training and inference performance, GPU efficiency, distributed failure handling
Hyperscalers and GPU clouds
12Cloud platforms create preparation topics in fleet capacity, tenant isolation, networking and service reliability. Individual teams can own different layers.PREPARATION FOCUS: Fleet scale, schedulers, networking, capacity, reliability
Chip and platform vendors
10Hardware and platform teams provide context for studying architecture, kernels, compilers and interconnects. Use the specific role to choose the depth.PREPARATION FOCUS: Microarchitecture, kernels, compilers, interconnect, benchmarking
AI infrastructure scale-ups
11Infrastructure products provide context for serving APIs, workload orchestration, multi-tenancy and operating cost. These are study areas, not a shared interview rubric.PREPARATION FOCUS: Serving and training platforms, multi-tenancy, cost per token, orchestration
ML platforms at product companies
7Product-facing ML platforms connect data, model deployment and service reliability. GPU depth depends on the team and workload.PREPARATION FOCUS: ML platform, data infrastructure, serving reliability, developer experience