Prepare for an AI infrastructure career.

Learn GPU performance engineering through CUDA execution, memory layouts, GEMM scheduling, correctness tests and measured Triton projects. Includes CPU exercises and a compiled-baseline comparison.

Practice rate limiters, versioned stores, resumable iterators and worker pools with explicit correctness rules, boundary tests and production extensions.

Prepare with Anthropic’s official hiring guidance, its historical performance exercise and practical serving problems. Separate documented assessments from study recommendations.

Prepare for CoreWeave infrastructure roles with official CKS documentation, Go concurrency exercises and GPU placement reasoning. Confirm role-specific interview details with the recruiter.

Work through a dense-model training estimate, memory placement, collective traffic, failure recovery and cost. Separate illustrative assumptions from a deployable cluster plan.

Derive Llama 3.1 70B KV-cache memory with explicit units, check a per-GPU budget, and explain paging, prefix reuse and quantization without confusing memory capacity with throughput.

Derive model memory, KV cache, training time, roofline bounds, collective communication and token cost with explicit units and limits.

Use NVIDIA’s official hiring process and engineering documentation to prepare for GPU library, inference-server and fleet roles, with practical profiling and systems exercises.

Use OpenAI’s official hiring guide and an HPC role description to prepare systems coding, performance reasoning and project discussions without assuming one fixed loop.

Diagnose a slow all-reduce with per-rank timelines, a worked bandwidth bound, matched NCCL benchmarks and evidence that distinguishes transport problems from lost overlap.

A practical GPU optimization workflow: establish correctness and timing, distinguish theoretical bounds from bottleneck evidence, and report reproducible results.