Your ML degree prepared you to write papers.
Not to build systems that run at scale.
Here's the uncomfortable truth: there is a growing gap between what universities teach about machine learning and what frontier labs actually need you to know.
Your coursework covered backpropagation, loss functions, maybe even transformers. You can derive the attention equation on a whiteboard. You got an A.
Then you open a job posting at DeepMind, Anthropic, or OpenAI.
What the interview actually asks
- Why does your training run OOM at 7B parameters but not at 3B — and what would you change without reducing batch size?
- Walk me through how FSDP shards optimizer states across 64 GPUs. Where are the communication bottlenecks?
- Your model's loss spikes at step 40K. Here are the logs. Diagnose it.
- Explain why speculative decoding gives you 2-3x throughput without changing model quality.
No course taught you this. Not because your professors were bad — but because this knowledge lives in internal docs, tribal knowledge, and hard-won debugging sessions at labs that don't publish tutorials.
Right now, someone with a similar background to yours is getting offers you aren't — not because they're smarter, but because they understand systems. They know why KV cache matters more than attention theory. They can estimate FLOPS before writing a single line of code. They debug distributed training runs the way you debug Python scripts.
The gap between "knows ML" and "can ship ML at scale" is where careers are made. And it's widening every month as models get larger, training runs get more expensive, and labs raise the bar.
We built the course we wish existed.
ML Systems.dev is not another "intro to deep learning." It's not a certificate mill. It's not a video series where someone reads slides at you for six hours.
It's the systems-level understanding that separates research engineers who get hired from those who keep applying. Every lesson follows the same arc:
Start with the real failure mode — the OOM, the NaN, the 3x slowdown
Diagrams, invariants, and the intuition that transfers across frameworks
50-150 lines of executable Python — no hidden abstractions
What changes at 70B parameters? At 512 GPUs? At 10K QPS?
Every lesson has runnable code. Every concept links back to how it's actually done at top labs. No hand-waving. No "left as an exercise for the reader."
After this course, you'll be able to
- Read a training config and estimate memory, compute, and time to completion
- Debug distributed training failures from first principles
- Explain the full inference stack — from KV cache to continuous batching to speculative decoding
- Speak the language that senior research engineers use in design reviews
- Walk into a frontier lab interview and hold your own on systems questions
The knowledge is out there, scattered across papers, blog posts, and the heads of people who don't have time to teach. We spent months collecting, structuring, and building interactive lessons around it — so you don't have to piece it together yourself.
The bar is high. But it's not unreachable. You just need the right material.
No signup required. Start with Track 0.