u/DustSavings976

I'm a 3rd year cs student doing research in graph neural networks and causal inference (heavy math, custom architectures). but when i look at internships and junior roles right now, 90% of them are just asking for "experience with openai api, langchain, and rag".

are companies still hiring junior engineers to actually build and train specialized models (gnns, cnns, custom transformers), or is the entire entry-level market just prompt engineering and api wrappers now?

feeling kinda demotivated about studying the deep math if the industry just wants api wranglers right now.

i've been deep in the GNN trenches for a recsys research project (lightgcn etc) and they look insane on benchmark datasets.

but every time i talk to industry bros they just laugh, flatten the graph features, and throw it into xgboost or a basic two-tower setup because the inference overhead is brutal for like a 0.5% metric bump.

is anyone here actually running graph neural nets in prod? does it ever actually justify the infrastructure nightmare or are we all just pretending tree models aren't still king?