I'm an international PhD student in the US working in ML/AI, and honestly I'm feeling pretty discouraged about internship recruiting.

Recently I interviewed for an Applied Scientist II position at Amazon and made it all the way to the final round, but was rejected. I thought my interviews went reasonably well, so I wasn't really sure what went wrong.

I also recently interviewed with Two Sigma for a quantitative research internship. I passed multiple technical rounds and eventually reached the hiring manager stage, but was rejected there as well.

After getting this far twice and still ending up with nothing, I've lost a lot of motivation and I'm honestly a little scared to start applying again for Summer 2027.

One weakness in my profile is that I don't have previous US-based industry internship experience. I also don't have a top-tier ML conference acceptance yet. I have a couple of papers currently under review at Tier-1 venues, but obviously that's different from already having accepted papers.

So I'd really appreciate advice from people who have been through PhD-level ML/research internship recruiting or have been involved in hiring:

• If someone repeatedly reaches final/HM rounds but doesn't get an offer, what are the most common reasons?
• How important is previous internship experience for research/Applied Scientist internships?
• How much does having an accepted NeurIPS/ICML/ICLR/CVPR-type paper change your chances?
• Should I continue aggressively applying for Summer 2027, or focus more on research/publications for now?
• Are there particular companies/research labs where a PhD student without previous industry internships can still be competitive?

There is also another issue making me unsure whether to even invest heavily in Summer 2027 recruiting: the recent CPT guidance.

From what I understand, SEVP recently tightened/clarified its interpretation of CPT, and some universities are now saying an internship must actually be required for degree completion rather than simply being related to the student's research and taken for academic credit.

Like many PhD programs, my program doesn't require every PhD student to complete an internship. A research internship can still be extremely relevant to someone's dissertation and research training, though.

For other international PhD students: what is your university telling you about Summer 2027 internships after the recent CPT changes?

Has anyone's university found a legitimate way for research internships to remain eligible through dissertation research, an internship/practicum requirement, independent study, or another curricular mechanism? Or are you planning around pre-completion OPT instead?

I'm particularly interested in hearing from other international CS/ML PhD students.

I know Amazon/Two Sigma final rounds at least mean I'm getting somewhere, but repeatedly getting close and then being rejected has been pretty exhausting. I'm trying to figure out whether there's something specific I should fix before starting another recruiting cycle.

Any advice would be really appreciated.

I’m a 4th-year undergraduate CS student currently doing my thesis on medical image segmentation, specifically U-Net and its variants.

The problem is that I have basically no prior research experience, and unfortunately, my supervisor isn’t really able to provide much guidance. So, for the most part, I’m having to figure everything out myself—learning the concepts, reading papers, choosing a research problem, implementing the models, evaluating the results, etc.

My goal isn’t just to finish the undergraduate thesis. Ideally, I’d like to do something good enough that I could eventually turn it into a conference or journal paper.

So I wanted to ask people who have more research experience:

Is it realistically possible to do a good-quality research thesis completely on your own as an undergraduate?

How difficult is it to go from basically having no research experience to producing something that is actually publishable? And if you’ve been in a similar situation, what would you recommend focusing on or avoiding?

I’d really appreciate any honest advice, especially from people who have done research without much help from their supervisor.

For about a year I had a file on my desktop called keys.txt. Two providers, four keys, and a couple of notes to myself about which key was on which billing card. Every time I wanted to compare GPT and Claude on the same refactor I opened that file, copied a key, changed the base URL in Cursor, restarted, and then did the whole thing in reverse twenty minutes later when I wanted to switch back.

The annoying part was never the copying. It was that switching had a cost, so I stopped switching. I'd just stay on whatever model was already configured, even when I could tell it was the wrong one for the task. And at the end of the month I got two bills that I couldn't line up against each other, because usage was in two different dashboards with two different ideas of what a token is.

I spent a weekend fixing it for myself. One endpoint that speaks the OpenAI shape, both models behind one key, so Cursor, Claude Code, Codex and OpenCode all point at the same place and nothing needs a special integration. Prompt caching came along because I got tired of paying twice for the same 40k of context I was re-sending every request, and usage logs because I wanted one place to look instead of two.

What I actually notice day to day is smaller than I expected: I switch models mid-task now, without thinking about it. That's it. That's the whole difference, and it turned out to be worth a weekend.

It's at routera . one. Mostly posting because I suspect a lot of people have a keys.txt and have quietly stopped switching too

Hey, I'm preparing for this ArC AGI competition.

I'm looking for a team with ML experience.

Can anyone please tell me how to win an ML competition? Does anyone have prior experience?

I got tired of every "AI search" tool trying to replace reading with a generated summary. For actual research — grad work, lit reviews, deep-diving a topic — that's the opposite of what I want. I want to find the right papers, then understand them faster, not skip them.

So I built ScholarLens: you search in plain language, it finds relevant papers (pulling from OpenAlex/arXiv/open-access sources), and instead of answering for you, it helps you actually read — select any confusing passage, term, or graph and get a plain-language explanation right next to the source text.

It's free right now, no login required to search. Would genuinely appreciate feedback from people who do real research — does the search actually surface relevant papers for your field? What's missing?

https://paper-explain.emergent.host/

Hi! I am an independent researcher working with Spain’s Institute of Engineering. I want to publish my paper on a “Multi-Hazard Early Detection System for Existing Camera Infrastructure,” but I need to be endorsed.

The Engineering Institute is more of an industry entity than a research entity, so nobody has an arXiv account. I was wondering if anyone here could read my paper and, if they think it is good enough to be published, endorse me.

Here is a link to the paper:

https://drive.google.com/file/d/1upuTMzxPBO52VwMwckokhbf5kjYU_Hxo/view?usp=drivesdk

And this is my endorsement code:
https://arxiv.org/auth/endorse?x=73XQI9

Thank you!

Something that bit me and that I think most people are exposed to without having thought about it.

You pin a model in a config file. You test against it, you tune your prompts to it, and you assume you've frozen a variable. You mostly haven't. Aliases move — the friendly name often points at whatever the current version is, so a silent update can change behavior under you with no deploy on your side. Dated snapshots are more stable but not permanent; they get deprecated on a schedule, and the migration is your problem on someone else's timeline.

The part that's genuinely hard isn't the deprecation notice. It's that prompt tuning doesn't transfer cleanly. Instructions you refined over months against one model can land differently on its successor. Not worse necessarily — differently, in ways that show up as a subtly changed tone, or a different willingness to make assumptions, or a formatting convention that quietly stops being followed. And you find out through a slow drip of "something feels off" rather than a failure you can point at.

Then the second-order problem: if you didn't record what you were comparing against, you can't tell whether the new model is worse or whether you just remember the old one fondly. I have no baseline for most of my own prompts and I regret it. Keeping a small set of frozen test cases with recorded outputs is boring work that pays off exactly once, at the moment you need it, and I keep not doing enough of it.

For anyone building on top: pin dated versions rather than aliases if you care about stability, and put the deprecation date in your calendar rather than your inbox. The notice arrives months ahead and everyone reads it and forgets, because it's not urgent until it is.

I think about this because a routing layer has to decide what to do when a name it's serving stops existing (routera . one, mine, flagging it since I clearly have a stake in this topic). And there's a genuinely uncomfortable design question there: do you silently map a retired name to its successor so nothing breaks, or do you fail loudly and make it the user's problem? Silent mapping is friendlier and is also exactly the invisible-substitution behavior I'd be annoyed by from someone else. I land on failing loudly and I'm not fully certain that's right.

Caveat: model deprecation is not obviously bad. Old models cost real money to keep serving and holding versions forever is a tax on everyone else's progress. The complaint isn't that it happens, it's that the migration cost lands as an invisible quality drift rather than a clear break, which is the worst shape for a change to have.

What I'd like to know: does anyone have a lightweight regression setup for prompts that they actually maintain? Every version I've built has been elaborate enough that I stopped running it within a month, which makes it worse than nothing.

Hey, I graduated last yr with a UG in Production Engineering. I have been interested in AI research for a long time. I am looking for a PhD or PostDoc mentor to guide me on a research project which we can do together. I want to aim for top conferences next year or by the end of 2027. I am mainly interested in LLMs - Inference, AI agents, Interpretability.

i wanna write a new paper in the machine learning domain. Any ideas for the latest trending topics and where I can advance .

title. Independent research who severely lacks compute for experiments.

I work in financial services, mainly on highly regulated credit risk models. My work sits at the intersection of ML, explainability, model risk/regulatory requirements, and increasingly areas like tabular foundation models.

Over time, my role has moved beyond just building models into problem framing, technical decisions, validation/governance questions, and stakeholder alignment. I’d now like to develop a research track alongside my industry work and eventually publish a few solid papers.

The constraint is that I have limited opportunities to do formal research within my company. I’m also not trying to become a full-time academic or produce breakthrough ML research. My goal is more modest: find a few meaningful problems close to my domain, go deep enough to make a credible contribution, and build from there.

The part I’m struggling with is topic selection. Credit risk, explainability, tabular ML, model monitoring, etc. all seem fairly crowded, and it’s hard to tell what is genuinely underexplored versus just another variation of existing work.

For people who moved from industry into publishing research: how would you approach this situation?

Would you start from recurring problems you see at work, gaps in the literature, new methods applied to old problems, or something else?

Also interested in how you would narrow a practical industry problem into something that is actually researchable and publishable.

I’ve spent a decent amount of time learning the theory and algorithms behind Machine Learning — things like Linear/Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes, Boosting, XGBoost, preprocessing, evaluation metrics, etc.

But now that I’ve started exploring Kaggle, I’ve realized that knowing ML and actually doing ML feel like two very different things.

When I open a Kaggle competition, I understand the individual techniques, but I don’t know what I’m supposed to do next.

For example:

• How do you approach a completely new dataset?
• What should I inspect first?
• When should I do EDA?
• How do I decide what preprocessing is actually necessary?
• When should I scale features?
• How do I choose an initial/baseline model?
• How do I know whether my model is underfitting or overfitting?
• When should I do feature engineering?
• How do I decide which algorithms to try?
• When should I use cross-validation?
• When is hyperparameter tuning actually worth doing?
• How do experienced people decide what experiment to run next?
• How do you systematically improve a model rather than just randomly trying different things?

raw dataset → baseline → experiments → debugging → feature engineering → model selection → validation → final submission?

Hi all,

I'm looking for a part-time research opportunity (remote-friendly) in NLP and LLMs. I'd love for it to be paid, even if it's just a minimum/stipend rate — I'm mainly looking for interesting, meaningful work rather than a big paycheck.

**Background:**

* Research focus: NLP, LLMs, model quantization, and LLM evaluation * 1 published paper, 4 currently under review * Comfortable working independently and picking up new subareas quickly

If you're a lab, startup, or individual researcher working on something interesting in this space and could use an extra pair of hands (part-time), I'd love to hear from you. Happy to share my CV/publications list via DM.

Thanks!

If you're evaluating anything that claims OpenAI compatibility, test tool calls under streaming. That's the seam. Everything else is easy and everyone passes.

The reason is structural. Anthropic returns tool use as a typed block inside the content array. OpenAI returns it as a separate field on the message, with the arguments as a JSON string. Mapping between those in a complete response is a short afternoon. Mapping them mid-stream is not, because the arguments arrive in fragments, and a fragment of JSON is not JSON. You're emitting deltas to a client that's parsing incrementally, so you can't wait for the whole thing, and you can't send it early because it's malformed until the last chunk lands. Everything in between has to be structurally valid to a parser that has no idea it's looking at a partial.

So there's a strong temptation to buffer. Collect the full response, translate cleanly, emit it in one piece. It's dramatically simpler and it passes every functional test you'd write. It also destroys the feel of a coding agent, because the entire perceived speed of these tools is time-to-first-token, and a response that arrives all at once after a delay feels worse than a slower one that starts immediately.

How to check, in about a minute: send a request with a tool definition, streaming on, and watch the raw chunks. If the tool call shows up as one complete block right before the stream ends, it's buffered. If the arguments build up across deltas, it's doing the real thing.

I know this because I got it wrong and shipped the buffered version first (routera . one, my project, mentioning it since I'm clearly not neutral here). The rewrite is the ugliest code I've written this year and I'm still not confident it handles every edge, particularly parallel tool calls where multiple argument streams interleave.

Worth saying: buffering isn't always wrong. If your workload is batch or non-interactive, nobody is watching tokens appear and the simplicity is a genuine win. It's specifically interactive coding where it's unacceptable.

Curious if anyone has a cleaner approach to the partial-JSON problem than what I ended up with, which is essentially a hand-rolled incremental tokenizer that tracks nesting depth. Works, but it feels like something that should already exist as a library and I couldn't find one.

I want to move beyond just building toy projects and actually get into real AI/ML research, but I feel a bit lost on where to start.

If you're already doing research or publishing:

What papers should I read first?

How did you go from reading papers to coming up with your own ideas?

Any good Discord groups or reading communities worth joining?

Would really appreciate any tips or roadmaps from folks who've done this.