u/grishma_1503

I have found MLIR, LLVM quite intresting for past 4-5 months but haven't dived deep yet, but from my experience as a AI systems engineer(i was responsible for building the autograd and computational graph integration into the main c++ DL framework, mostly runtime focused) i am familiar with the concepts of IR dialects and stages of lowering through the compiler pipeline toward machine code by exploring the pytorch and tensorflow compiler architecture(conceptual familiarity from studying compiler architectures) as i was incharge of the runtime mechanics.
(i am conceptually strong with advanced cpp and most of the runtime stuff as i built the framework with ai-assistance)

i had read the frst 2 chapters of toy mlir and first 5 chapters of the https://book.mlc.ai/ and have some base understanding of the IR so far. once i started reading these two resources i could get quite the grasp about how the mechanisms work under the hood of the ML compiler.

its been 2 months since i left the job and i want to transition into compiler engineering in the ML field.
given my background, what would be the best path to become employable as an ML compiler engineer?

i’m building a secret-scanner triage system that decides whether a detected credential should be treated as a real exposed key or a false positive/test fixture. i’m modelling that decision under uncertainty, and it looks like the right policy mostly comes down to one ratio: the cost of dismissing a live key vs. the cost of rotating something that turns out to be a test fixture and accidentally breaking a consumer. my sweep puts the policy flip somewhere between 5:1 and 100:1. has rotating something ever actually broken things for you?