r/MachineLearning

Today I was just casually browsing some jobs with tags [machine learning] on one of those large popular job-sites. What I am seeing really had me astonished. I want to check with Reddit whether I am hallucinating.

A non-FAANG/non-Deepmind/.../non-Anthropic industrial automation company is hiring people to work on ML for robots (the latest hot topic). Fine. But then I saw their laundry list of job requirements ("you must meet these"), which include:

• Deep expertise in LLM, VLA, VLM, action transformers

• Deep expertise in robot dynamic and kinematic modelling (forward, inverse kinematics, trajectory generation, planning), sensor fusion, model predictive control, reinforcement learning

• Deep expertise in CUDA GPU programming, FPGA hardware acceleration

• Familiarity with latest software engineering best practices in Python3 and C++23

• Familiarity in one or more of popular ML framework

• Have top publications in one or more typical ML and robotics conferences

This is before they go off listing familiarity with a set of standard softwares/simulators, one of which is called RLib, something I've never heard of. Oh and of course they had these 3+, 5+ "non-academic" experience requirements. I forgot which is which.

I was just sitting there confused. Then I checked several more jobs, and it was more of the same (except for some banks).

I remember there was a talk by Terence Tao where he divided mathematician into two camps, the analysts and algebraists. He said even among top mathematicians, it is exceedingly rare to find someone who possess deep expertise in both, as each tends to require a different mode of thinking and each is infinitely deep in terms of specialization, theory and insights.

And here we have a bunch of ML companies treating these infinitely deep academic fields ranging from robot dynamic and kinematic modelling to large language models like some bizarre MMORPG video-game scenario where you need to be a warrior archer warlock who is also a shaman priest mage.

Who are they even hiring, lol?

Edit: the original post targeting Chinese researchers is removed by the mods. Points made here are responding to that particular post. So when you leave comments to this post, please do realize that there's particular context that's not available now. Sorry for any confusion.

Although the original post I'm calling out is taken down, I do think it's an important topic, and choose to keep my post unchanged.

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Yes, I'm calling it out. It IS racism. As an active member of and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber.

I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups.

This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field.

Edit: Since the post sparked some heated debate, I elaborate a bit. In the comments, some are like "you might be good, but I had this/that bad experience with Chinese..."

Sound familiar? This is exactly the type of comment racists make to justify racism. We have a systematic failure in the peer-review system and whether a paper/reviewer comes from China does not play any major role contributing to this failure. In a math- and data-driven sub, normalizing such claims is unbelievable and unacceptable. This IS racism.

“Maybe the real AGI was the friends we made along the way” is a sentiment that always hits me, and conferences are the places where I reunite with old friends and meet new ones. However, when it comes to the submission/review experience, it might not be much of an exaggeration to say that almost everyone has many unpleasant experiences to share.

So I wrote a position paper to discuss this. I argue that current conference organizers lack proper tools to instill accountability and incentives for reviewers/authors/ACs/SACs… The result is that undesired behaviors (e.g., lack of engagement) often go unchecked, while good behaviors are rarely rewarded and therefore don’t happen (honestly, when was the last time you witnessed any constructive internal discussion among reviewers/ACs?). And this won’t change by writing nice words in Reviewer Guidelines or issuing a few desk rejections.

I propose a CREDIT SYSTEM where community members earn points by “doing good” — e.g., reviewing a paper would get you +1, being outstanding gets you +3. Then, members can spend points to redeem perks ranging from traditional ones already adopted in current ML conferences (e.g., free registration) to new ones, such as requesting an additional reviewer to sort through a muddy situation. Such a system could also support explorative ideas like:

- Refundable submission fees: say 10 points per submission, which are then refunded regardless of acceptance, unless the submission is uniformly voted to be unready / ultra-low quality.

- Mobilizing non-author reviewers: non-author reviewers don’t have the bandwidth issue of wearing both the author and reviewer hats and are not influenced by their own submissions. 

and many more...

My proposed system is far from perfect, but I’d like to think it takes a step toward a better conference review mechanism. I am also glad to see the position paper track becoming a welcoming platform for researchers to hash out their proposals and build toward a better future (see other review-related position papers below.)

For a topic that affects literally everyone at ICML, I am eager to hear your thoughts.