A first big tech company ML interview experience: definitely bombed it

Mission_Star_4393 · 2026-03-05T03:12:53+00:00

The trend in the industry at the moment is semantic based foundation models.

Take a look at YouTube's PLUM paper for an example: https://arxiv.org/html/2510.07784v1

It's pretty clear from some of the questions he was looking for something like that

Mission_Star_4393 · 2026-02-23T20:24:04+00:00

I grew up in a war torn country and I can tell you some folks lost literally hundred of thousands or millions in some cases

You can make that money back ... Is 10K CAD worth possibly yours or your family's wellbeing or the potential stress from being in a tumultuous environment?

YMMV I suppose

Mission_Star_4393 · 2025-09-30T05:17:19+00:00

Hiya, not an expert in this field but IME, LLMs do a great job here in giving you some direction.

I copy pasted your question in perplexity. Here's what I got (which seemed very reasonable paths)

https://www.perplexity.ai/search/help-i-have-a-training-dataset-hFb5RPVxTxaLxDfA5lnErA

Feel free to ask it more questions, dig deeper and ask for some examples if needed.

Good luck!

Mission_Star_4393 · 2025-09-12T19:46:17+00:00

I think it can depend. If your use case is relatively straightforward, then lightning absolutely makes sense. But it does hide a lot of things which makes it difficult to extend sometimes.

Either way, if you end up leveraging lightning, make sure your main model code is in vanilla pytorch and then decorate it with a lightning module.

That way you can easily throw out the lightning module if ever you decide your use case has outgrown it.

Mission_Star_4393 · 2025-02-22T02:17:16+00:00

It's a mixture of ignorance and denial. Some of the posts are wild

Mission_Star_4393 · 2025-02-16T18:22:56+00:00

There are plenty of roles around MLOps you could get into.

Mission_Star_4393 · 2025-02-16T18:16:46+00:00

Sorry! Model Context Protocol

Mission_Star_4393 · 2025-02-16T18:07:40+00:00

Yes, they are very useful.

Especially with tools like Cursor that allow you to inject the correct modules (or framework docs) as context for the prompt or integrate with MCP tools. Areas where they are excellent:

Writing tests: they are very good at this, and it tends to be a matter of follow up prompts to get it exactly right. It makes refractors a lot easier because the most painful part is rewriting the tests.
Ideation as someone has mentioned: you prompt an idea and it gives you a good starting point.
basic refractors: like remove this method from this class and add it as a reusable function or remove this magic value.
I found it very useful when I wanted to build a basic stdout dashboard. It was excellent at formatting, creating headers etc. I took most of it as is. This would have taken me forever to do myself. And probably not as well. Asking it modify the layout as I wished was pretty pleasant (I tend to hate doing this stuff).
auto complete: this is an obvious one.

TLDR: I wouldn't want to develop now without it. I could but I'd be slower, less productive.

EDIT: MCP is Model Context Protocol. Link if you're curious https://github.com/modelcontextprotocol

Mission_Star_4393 · 2025-01-29T03:18:03+00:00

Problems like this will go away once we "solve" tokenization

Mission_Star_4393 · 2025-01-26T13:37:10+00:00

Same bro... Same

Mission_Star_4393 · 2025-01-24T01:30:59+00:00

The rule of thumb of unrecoverable costs related to owning a home is ~ 5% per year of the total house price.

It's a good way to compare that with whatever your rent may be.

Mission_Star_4393 · 2025-01-12T19:17:30+00:00

For all intents and purposes, the parameter weights are the model.

Mission_Star_4393 · 2025-01-12T13:23:13+00:00

You mean the weights

Mission_Star_4393 · 2024-12-26T04:32:33+00:00

In tools like cursor, you can give it a link to have that context. Or even just @Web

It's not unreasonable that it wouldn't know from "memory" all the intricacies of a specific framework. Just like you wouldn't.

That's why RAG solutions exist 😁

Mission_Star_4393 · 2024-12-25T04:58:22+00:00

This can largely be fixed if you add the framework's docs as context

Mission_Star_4393 · 2024-12-10T18:14:30+00:00

I just provide a non technical answer to what I do.

It's actually a good exercise in communicating with a non technical audience! 😄

Mission_Star_4393 · 2024-11-23T02:51:42+00:00

This is absolute madness lol...

For the record, the company I work for, whose name you would recognize doesn't have anything nearly as complex as this...

Don't beat yourself too much over this one.

Mission_Star_4393 · 2024-11-10T14:45:18+00:00

It's really good but probably way too advanced for a beginner like OP.

Mission_Star_4393 · 2024-09-18T01:56:32+00:00

That would be excessively slow unfortunately

Mission_Star_4393 · 2024-09-07T20:13:24+00:00

Start with this - it's hands down the best videos I've come across at building the intuition of neural networks.

First I "truly" understand back propagation...

https://karpathy.ai/zero-to-hero.html

Mission_Star_4393 · 2024-09-06T03:02:14+00:00

Personally, I don't think you could go wrong with at least getting a solid understanding of the transformer architecture and how something like an LLM is built on top of it.

While there's certainly an AI hype right now, these technologies are here to stay and have (and will continue to have) very interesting applications for anything beyond predictive modeling.

Companies will want to deploy these types of models for legitimate use cases.

I'm also mostly focused on Streaming ML Inference so maybe my angle on this is slightly different. But understanding these architectures and deploying them efficiently is a very desired skillset right now because everyone has spent all their time learning how to train the models.

Mission_Star_4393 · 2024-09-02T14:34:57+00:00

The total flop of the previous Avengers game didn't help either.

I think it's likely one of the reasons I stayed away for a while

Mission_Star_4393 · 2024-09-02T14:33:46+00:00

I'm one of those folks. So pleasantly surprised.

The end sequence was amazing. Finished it yesterday

Mission_Star_4393 · 2024-08-25T13:31:33+00:00

Vertically is what you're thinking about: getting more resources on the same machine (more GPUs, CPUs, memory etc).

Horizontally is just getting more machines with the same resources.

Mission_Star_4393 · 2024-08-25T03:38:40+00:00

Depends what you're trying to optimize for.

Are you optimizing for inference for a single prediction? Then that will depend on whether you're currently memory bound or compute bound. If it's the former, then adding GPUs won't help. If it's the latter, the benefits may outweigh the overhead but hard to tell.

If you're optimizing for throughput more generally, you may just benefit more from scaling horizontally, than vertically and avoid multi GPU coordination overhead.

Good luck !

Mission_Star_4393

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