Foundational CS for ML Engineers
Most ML engineers learned ML from the top down - frameworks first, theory second, systems never. That works until it does not. When your training run is mysteriously slow, when your inference server OOMs at 4am, when you need to write a custom CUDA kernel or explain to a system design interviewer why your architecture makes specific memory access patterns - that is when the gaps show up.
This track is not about making you a systems programmer. It is about giving you enough CS foundations to be a dangerous ML engineer.
The Problems This Track Solves
"Why is my model slow?" Usually the answer is not in the algorithm. It is in memory access patterns, cache misses, thread contention, or memory bandwidth saturation. You cannot debug this without understanding the hardware and OS layer.
"Why does my training keep OOMing?" Understanding Python's memory model, GPU memory allocation, and the difference between device memory and host memory turns a three-hour debugging session into a five-minute fix.
"How does torch.compile actually speed things up?" The answer is in compilers - how they fuse operations, eliminate redundant memory reads, and generate optimized code for specific hardware. You do not need to write compilers to understand this, but you do need the vocabulary.
"What is the right data structure for a 1B-row feature lookup table?" Algorithmic complexity matters when your data stops fitting in memory. An O(1) average vs O(log n) lookup sounds academic until you are serving 100k requests per second.
Seven Modules
| Module | Topic | Why It Matters for ML |
|---|---|---|
| 1 | Computer Architecture | Why GPUs are fast for matrix ops; cache locality in training loops |
| 2 | Operating Systems for ML | Memory mapping large datasets; process/thread tradeoffs in data loaders |
| 3 | Compilers and Runtimes | How torch.compile, XLA, TensorRT make your code faster |
| 4 | Memory Management | OOM debugging; Python GC; zero-copy data pipelines |
| 5 | Networking for Distributed AI | AllReduce bandwidth; gRPC for serving; RDMA basics |
| 6 | Algorithms for ML | Complexity of attention; ANN data structures; sampling at scale |
| 7 | Systems Programming | Writing Python C extensions; Cython; custom PyTorch operators |
Who This Track Is For
ML Engineers who feel their systems knowledge is a weak spot.
Engineers transitioning into ML from backend or infrastructure - your existing CS knowledge applies directly here.
Senior engineers preparing for staff-level interviews where systems design depth is expected.
Anyone who has ever stared at a profiler output and not known what they were looking at.
How to Use This Track
You do not need to complete modules in order. Each module is self-contained. Navigate to the area that addresses your current gap.
If you are debugging slow training: start with Memory Hierarchy and Caches.
If you are dealing with OOM errors: start with GPU Memory Allocation Patterns.
If you are building a serving system: start with gRPC for Model Serving.
If you are writing custom ops: start with Writing Custom PyTorch Operators.