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How to become an AI Systems Engineer
GPUs, distributed training, kernels, and memory and throughput optimization. Make large models train and serve fast.
- 5phases in the roadmap
- 15topics to work through
- 77graded practice questions
- Freeno payment, ever
The AI Systems Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1AI systems foundations
GPU and accelerator basics
A GPU is a throughput machine: thousands of lightweight cores execute the same instruction across many data lanes (SIMT), so it dominates wide, regular, data-parallel work like matmuls but stalls on…
Memory hierarchy
GPU memory forms a hierarchy: large but slow off-chip HBM (global memory), then L2, on-chip shared memory/L1 (SRAM), and the fastest registers.
Performance fundamentals
The roofline model classifies a kernel by its arithmetic intensity (FLOPs per byte): kernels under the memory-bandwidth roof are bandwidth-bound and need less data movement or more reuse, while those…
Phase 2AI systems training
Distributed training
Synchronous data-parallel training replicates the full model on every GPU, gives each a different micro-batch, and all-reduces gradients each step so all replicas apply identical updates.
Parallelism strategies
When a model exceeds one GPU, you shard it: tensor parallelism splits layers' matmuls (heavy all-reduce, needs fast intra-node links), pipeline parallelism splits the model into sequential stages…
Mixed precision
Mixed-precision training uses 16-bit math for speed and memory while keeping FP32 master weights and accumulation for stability.
Phase 3Kernels and compute
CUDA basics
CUDA organizes work into threads grouped into warps of 32 and blocks; the hardware coalesces adjacent-thread, adjacent-address global-memory accesses into few wide transactions.
Kernel optimization
Kernel optimization cuts memory traffic and launch overhead: fusing operations and tiling work in fast on-chip SRAM avoids HBM round-trips.
Profiling
Profiling turns optimization from guesswork into evidence: tools like Nsight Systems/Compute (or a framework profiler) reveal the real timeline of kernel durations, GPU idle gaps, host/device…
Phase 4AI systems inference
Serving systems
Modern LLM serving replaces static batching (gather a fixed batch, run all to completion) with continuous (in-flight) batching that admits and evicts requests at token-step granularity, so finished…
Batching
Batch size is the primary lever between throughput and latency: larger batches raise aggregate tokens-per-second and cut cost per token but add queuing and per-request latency.
KV cache
The KV cache stores past keys and values so decode avoids recomputing attention, but naive contiguous per-request allocation fragments GPU memory and causes OOM even when memory is free.
Phase 5Scale and reliability
Cluster management
Tightly-coupled distributed jobs need gang (all-or-nothing) scheduling so they start only when all requested GPUs are available together, with topology-aware placement co-locating ranks on fast…
Failures at scale
At thousands of GPUs over weeks, component failures are statistically expected, not eliminable.
Cost
For an AI systems fleet, effective cost is dominated by whether expensive accelerators are actually working: idle GPUs are the biggest waste.
Reading for this path
The primary sources behind the topics above, all free to read.
- NVIDIA: CUDA C++ Programming Guide
- NVIDIA: GPU vs CPU architecture
- NVIDIA: CUDA C++ Programming Guide - memory hierarchy
- NVIDIA: CUDA best practices - memory optimizations
- Roofline: an insightful visual performance model (CACM)
- NVIDIA: Nsight Compute roofline analysis
- PyTorch: Distributed Data Parallel
- PyTorch: distributed overview
- arXiv: Megatron-LM tensor parallelism
- arXiv: Efficient Large-Scale Training on GPU Clusters (3D parallelism)
- arXiv: Mixed Precision Training
- PyTorch: automatic mixed precision (AMP)
- NVIDIA: How to access global memory efficiently (coalescing)
- arXiv: FlashAttention
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
AI UX Designer
Human-AI interaction patterns, trust, uncertainty, and feedback loops. Design interfaces where people and models work well together.
Analytics Engineer
Data modeling, transformation pipelines, metrics, and data quality. Turn raw data into trusted datasets teams can build on.
Applied Scientist
Model adaptation, evaluation, and prototyping against real product problems. Take research from paper to production.
Computer Vision Engineer
Image and video models, VLMs, detection, and visual inspection. Ship systems that see and understand the world.
Context Engineer
Context windows, memory systems, retrieval strategy, and token budgeting. Get the right information in front of the model every time.
Conversational AI Designer
Dialog flows, persona design, voice and chat UX, and escalation paths. Craft conversations people actually want to have.
Start the AI Systems Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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