AI Infrastructure Engineer Skills: A Practical Preparation Map

AI infrastructure skills fall into several work areas: accelerator performance, distributed training, inference services and fleet platforms. You do not need equal depth in all of them before applying. Choose a role, identify the system it owns, and build evidence that you can reason about that system.

For example, the OpenAI HPC role emphasizes software for fleet operation, while Anthropic’s GPU role calls for performance engineering. These reviewed postings support different preparation priorities. They do not establish a common interview rubric.

Consider an illustrative dense model with exactly 70 billion parameters stored at two bytes per parameter. Weights alone occupy 70 × 10⁹ × 2 = 140 × 10⁹ bytes, or 140 GB in decimal units. That exceeds an 80 GB device before accounting for KV cache, activations and runtime memory. Dividing by two devices gives a weight-only average of 70 GB per device; it does not prove that a specific partition and runtime will fit.

Now change one assumption. One byte per stored parameter halves the weight-only estimate to 70 GB, but quantization metadata and runtime buffers still need space, and numerical quality must be checked. This is the useful skill: derive a bound and explain what it omits. The model memory question, formula sheet and KV-cache calculator extend the reasoning.

These are study exercises, not confirmed questions from any employer.

Learn how threads map to execution units, how data moves through registers and device memory, and which operations use specialized matrix hardware. Then connect that knowledge to one observation: uncoalesced loads move unnecessary bytes, an oversized working set reduces reuse, or a synchronization point leaves other work idle. The GPU architecture track starts with those mechanisms.

For training, add collective communication and parallelism choices. For serving, add prefill, decode and cache management. For platform work, learn placement constraints and GPU health signals. Switch the depth of your study when the target responsibility changes; do not treat a list of framework names as a syllabus.

A bounded request queue, a rate limiter or a versioned key-value store makes useful practice because the correctness rules can be tested. State what happens on an empty input, a duplicate request, cancellation, overload and a retry. Add concurrency only after the single-worker behavior is clear. The coding systems track supplies exercises with those extensions.

Use a language allowed by the recruiter and suitable for the work. General platform code may use Python or Go; a kernel task may specify CUDA, HIP or another accelerator language. Do not infer the allowed interview environment from the languages in a job listing.

Write down the workload and the initial problem. Show how you measured it, why you chose a change and what would make you reverse that choice. If a GPU experiment is too expensive, begin with CPU tests, trace analysis or a simulator and state that limit. Obtain the provider’s current price and impose a spending limit before renting hardware; there is no fixed budget that covers every multi-GPU exercise.

Finish with an artifact another person can run: code, inputs, tests and a short explanation of the result. Include a failed approach if it explains the decision. The study paths help sequence the background, while hands-on exercises give smaller tasks to complete before a larger project.