Hallo zusammen,
ich wollte mich mal an diejenigen wenden, die in Machine Learning, KI oder einem verwandten Bereich promoviert haben.
Mich würde interessieren:
Wie war euer Einstieg nach der Promotion?
Was habt ihr als Einstiegsgehalt bekommen?
Und wie schätzt ihr aktuell den Arbeitsmarkt für Leute mit Promotion in ML/KI ein?
Danke.
I keep hearing some version of this:
“A paper that got accepted years ago wouldn’t stand a chance today.”
Honestly, for a lot of ML subfields, this doesn’t sound crazy anymore.
A paper that once looked solid can now look under-evaluated, under-ablated, weak on baselines, or just too obvious.
So maybe the real claim is:
A mediocre accepted ML paper from years ago would probably get rejected today.
Do people agree? Has the bar actually gone up, or has the field just become more crowded and more competitive?
I know publication count is not everything, and quality, contribution, advisor/lab culture, subfield, and luck all matter a lot. But to make the comparison easier, I’m curious about the publication-count side specifically.
For an ML PhD, what would you consider an average publication outcome by graduation?
For example, would something like 3–5 first-author papers at A/top-tier venues* be considered roughly average, or would that already be above average in ML?
By A*/top-tier, I’m thinking of venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, etc., depending on the subfield.
Important:
Again, I know paper count is a crude metric. I’m just trying to get a rough sense of what people in the field see as average, strong, or unusually strong.
I’ve been thinking about the current state of machine learning PhDs, including my own work, and I’d like to hear how others see it.
My impression is that a large fraction of modern ML PhD work follows a fairly predictable pattern: take an existing idea, connect it to another existing idea, apply it in a slightly different setting or community, tune the system carefully, add some benchmark results, and present the method as a new state-of-the-art approach. Another common pattern is mostly empirical: run benchmarks, report observations, provide some analysis, and frame that as the main contribution.
To be clear, I’m not saying this work is useless. Incremental progress matters, and not every PhD needs to invent a new paradigm. But sometimes it feels like many ML PhDs are closer to extended master’s theses: more experiments, more compute, more polished writing, and more benchmarks, but not necessarily a deeper scientific contribution.
What bothers me is that the same pattern appears even in top-tier conference papers. A paper may look strong because it has a clean story, a benchmark win, and good presentation, but after removing the “SOTA” claim, it is not always clear what lasting knowledge remains. Did we learn something general? Did we understand a mechanism better? Did we identify a failure mode? Did we create a reusable method or evaluation protocol? Or did we mostly produce another temporary leaderboard improvement?
I’m also reflecting this back onto my own PhD. I see some of the same patterns in my work, so this is not meant as an attack on others. It is more of a concern about the incentives of the field. ML seems to reward publishable deltas: small method variations, new combinations, benchmark improvements, and convincing empirical stories. But I’m less sure whether it consistently rewards deeper understanding.
So my question is:
Have ML PhDs become lower-quality compared to PhDs in other fields, or is this simply the normal shape of cumulative research in a fast-moving empirical field?
And maybe more importantly:
What separates a genuinely strong incremental ML PhD from one that is basically a collection of polished benchmark papers?
I’ve been trying to make sense of all the “ML conferences are a lottery” takes, and honestly I think it’s both true and not true depending on what you mean.
If a paper is clearly strong, like genuinely solid contribution, well executed, easy to understand, it usually gets in. And if it’s clearly weak, it usually gets filtered out. The weirdness people complain about mostly lives in the huge middle where papers are good but not undeniable.
That’s also where scale starts to matter. There are just so many submissions now that reviewers are stretched thin, matching isn’t perfect, and everyone has slightly different standards or taste. Add tight timelines and limited back-and-forth, and small things start to matter a lot. Whether a reviewer really “gets” your contribution, how clearly you framed it, or even just how it lands with that particular set of reviewers can swing the outcome.
I think that’s why it feels random. Not because the whole system is broken, but because a big chunk of papers are sitting right near the decision boundary, and decisions there are naturally high-variance.
People often from strong research groups don’t experience this. It’s more that they’re better at pushing their papers out of that borderline zone. Cleaner writing, stronger positioning, more predictable execution. So a larger fraction of their work is clearly above the bar.
So my current take is: it’s not a lottery overall, but it absolutely behaves like one near the cutoff, and that’s where most of the frustration comes from.