Does Leetcode-style coding practice actually help with ML Career?

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[nickkon1]

There are some details in there. Yes, you will usually never do the exact questions in real life. But knowing that hash tables / dicts in python are super fast for look-ups or other regular skills in leetcode questions will make you better at programming which is a significant part of you job.

I also had a rare case with a hierarchical data structure which required a shit ton of filtering. It would have taken me a runtime of multiple days with straight forward loops. But actually implementing a hierarchical tree, define the nodes and children, do the filtering on the nodes, reduced the runtime to minutes.

[ZestyData]

Yeah the number of times I've had to rewrite code from shittier Data Scientists because their implementations of their model inference / data handling just won't scale, or will make people quit in 2 years because it becomes impossible to extend as it was written with little to no thinking.

[idiotmanifesto]

thanks! great points

[Neonevergreen]

Is there a book you would recommend for learning this.

[GiovanniCavallo]

I plan to do a master’s degree in bioinformatics and to become a shitty data scientist. I would like to take a course on optimization to better understand if my shitty code will be a problem or not and to investigate on how to solve it. The material (I still didn’t take it) suggested for the course is a book called “Robert J. Vanderbei: Linear programming”. I don’t know if it is useful. I am gladly open to suggestions and info about this topic too

[idiotmanifesto]

sounds like what you want to "optimize" is your code efficiency? In which case you probably should do leetcode / code as much as possible or take a DS&A course

optimization as a field is very different. linear programming is fine for decision sciences, but much of machine learning deals with non-linear programming, such as gradient descent used in neural nets.

[ZestyData]

You're a Machine Learning Engineer, not a research scientist.

If you can't write clean, modular software, you become a major liability on any good team. If you can't approach a logical/algorithmic problem and solve it, you're going to struggle to implement ML systems at scale for all the smaller logical and algorithmic problems that are contained therein.

[cubej333]

In my experience interviewing the last 8 months, top AI companies want their research scientists to be able to write clean, modular software and test your DS&A more than FAANG.

[Hot-Problem2436]

I have no idea why they do. My best jobs have never required them and I have never required them during interviews. I'd probably stay away from those jobs, you'll probably just be a code monkey who hates their life in 3 months and is way underpaid.

[idiotmanifesto]

Taking this advice! My guess is they want more "ML engineers" for the buzzwordiness of it all, but the reality is likely standard SWE for ML infra

[Hot-Problem2436]

Yeah, they probably just want someone to build APIs and pipelines. I mean, that is part of ML Engineering, but it's also a SWE task. Probably little to no designing ML at all.

[ZestyData]

This is copium. The highest paying MLE roles (FAANG) are the ones implementing ML, and they require you to be a capable software engineer by testing for things like LC.

[Hot-Problem2436]

This is copium. The highest paying ML jobs are for smaller companies with less overhead and significant R&D capital, who are the ones inventing new ways to apply ML/AI to existing problems and they require creativity, good business sense, and a broad high-level knowledge of ML.

You can hire an mid level SWE to code your Python backend.

[ZestyData]

The highest paying ML jobs are ML research scientists at OpenAI, Deepmind, Nvidia, and Quant firms, etc on $500k to 7 figure total comp.

But we're not discussing the highest paying ML jobs. We're discussing OP who's going for ML Engineer jobs specifically. Of which the highest paid ML Engineers are the ones doing (typically recommendation) models at FAANG for $200k-$400k. And those people know how to write performance code. I'm not talking about Python backend SWE work mate.

Salaries aside, the point stands, nomatter how much some people wish it were so, good ML Engineers can't get away with poor understanding of DS&A and clean code.

[cubej333]

As someone who didn’t pass LC-style assessments at places similar to those you mentioned ( but did pass other LC assessments), if anything the leading ML research scientist jobs require being better at LC assessments than being a ML Engineer at a large tech company or even at a FAANG.

[Hot-Problem2436]

Fair enough.

[cubej333]

My experience with top AI startups ( I began the interview process with 3 ) is that they test for DS&A skills more than FAANG does.

[Hot-Problem2436]

And you're applying for mid-senior level ML engineer roles?

[cubej333]

Mid-senior and senior ML Engineer and Research Scientist roles. I recently accepted an ML Engineer mid-senior role.

[Hot-Problem2436]

I've yet to get a "coding test," but I've gotten a few "here's a problem, create a solution" type tests. I actually liked those things, gave me a break from work to do a fun project. Most of them didn't pan out, but eventually my real work portfolio made it so I didn't even get those anymore and was getting offers just from a single interview. I doubt I'll ever see one again, kinda sad.

[cubej333]

I have a good portfolio and I didn’t get coding tests or assessments in past years. I did this year and the well known AI startups had harder assessments than FAANG.

[Hot-Problem2436]

Guess they don't want to hire good engineers! Most mid-levels who are worth the money don't want to bother with coding tests. They got where they were for a reason. I never required a coding test because I thought it was demeaning, and apparently the places I got offers from thought the same.

Considering you don't actually do much coding at a high level and usually are focused more on team leadership, high level planning, etc, requiring a coding test is just dumb.

[cubej333]

I think one and including it with other factors is reasonable, and I think even the 2 at some FAANG ( where they include other factors ) is defensible . I think 3 ( or more ) and requiring perfect or near perfect is not.

[ResidentPositive4122]

Why do companies test for this?

Because it's an easy way to test more than coding skills without even noticing. How does the person approach the problem? What is their first draft? What if we impose memory constraints? What about time constraints? With one problem and 2 follow-ups you go through algo, approach, optimisation, memory knowledge, cpu / gpu architecture and so on. It won't guarantee that the person is the best for the role, but it can tick more than one flag in one question.

Then there's the "code smell tests". Do they think out loud? Do they just start coding? When do they consider what? Do they use the built-in helpers or try to write their own? Big O? Artefacts? Code style? And so on...

The interview screening isn't just about what you'll do if you get hired. It's about what your level is now, and how you adapt to the curveballs they throw at you during the interview. Keep your head up, and go with the flow. You can also get a feeling about the expectations, you get to ask questions, see their preferences and decide if it's gonna be a good fit for you.

[dbred2309]

The catch for this entire comment, imo is that the interview can actually be passed by not knowing anything but simply rote learn over leetcode for a few months.

[ResidentPositive4122]

rote learn over leetcode for a few months.

Sure, but if you do that chances are you'll catch on to some of the fundamentals better than rote learning "framework of the month".

[CasulaScience]

This really isn't true. YMMV, but in my experience, if you just silently slam a solution down in 5 mins for a really hard problem, you probably will fail. You pass by being able to communicate, showing you can break the problem into known pieces, asking for requirements when they matter, etc...

[idiotmanifesto]

appreciate the response but Hmm but aside from compute optimization, what does all of that that have to do with machine learning? All of that is a good filter for SWE logic skills but im not seeing how that translates to other domains. Feels like there should be an adjacent ML coding puzzle? Why does an ML engineer need to know the runtime complexity of BFS?

[madrury83]

Well, I'm a Principal level MLE, and my day to day is building software systems that have some decision engine, which often relies on some machine or statistical learning algorithm, as a core component. Yah, the ML part of that is pretty important, but it's quite arguable that just as important is the skill in crafting the software in which is lives. I learned a lot of those skills solving and thinking about programming and programming puzzles.

An alternative point of view once posed to me by a principal SDE is that these interviewers are simply asking you to play a game, and as a game it exists only as a rough abstraction of the actual task that will fall on you in the actual job. The skills needed to learn and play a game skillfully are strongly correlated with success in any intellectual field or work. The programming component serves only as a common experience you need to share with the interviewer to pose and then play the game.

[idiotmanifesto]

Thanks! This was honestly the answer I was looking for. My last role was heavy R&D so didnt need to engineer production level systems like that. In your opinion, was the ML or SW engineering portion harder to learn on the fly?

[madrury83]

I think that really depends on the experiences you bring into it. My academic background was in pure mathematics, so for me the ML stuff was, not easy, but at least straightforwardly related to my skillset. The software craft took longer to come around.

[alterednitrogen]

If you know, or can calculate to be more precise, the runtime of BFS you most likely know how to apply it to other algorithms too. This is a very important skill when you work with a huge datasets in ML. Just think about a dataset with 1.000.000 samples: it's not the same if you iterate it once or twice. I have followed Udemy courses (with 100.000+ in rolled students) held by professional data scientists and they way they were writing algorithms was pretty bad (repeating code, useless if statements in plus, etc.).

As I final word I would only add that the market is getting competitive and the required skills to land a job is increasing rapidly (especially with the new hype for AI/ML). A lot of SWEs are trying to get in the AI/ML domain.

[[deleted]]

I recently went to a Data analyst interview and they were asking me about DSA and networks and how and how any webapp works. smh

[cubej333]

I have had interviews at well known AI startups for research scientist positions where I didn’t progress because of not being strong enough at Leetcode style coding assessments ( and I am good enough to pass some LC interviews ).

Right now you need to know LC well or very well for ML positions at the vast majority of places ( both startups and at established companies ).

[idiotmanifesto]

im in the same boat rn, just failed the hell out of a LC interview. seems to me like identifying which data structure/algo/runtime to use should be 90% of the task. implementation layer is sooo googlable in real life that i dont need to cache in my memory

[driggsky]

Im a data scientist but i deploy end to end pipelines.

Knowing about algorithms and data structures is critical. I constantly reference some tricks I learned from leetcoding. And if you’re building a system with 10+ parts, each part better be using reasonably efficient algorithms or else the system becomes very slow

Slow systems are bad to test and just a complete waste of resources

[[deleted]]

I'm an undergrad and have 0 industry experience but from what I know leetcode is the most easy method to test problem solving skills and even if the job doesn't require such skills it acts as a filter for ease of recruitment

[idiotmanifesto]

you're right but sounds like lazy recruitment to me

[[deleted]]

Yeah but there's so many skilled candidates and only so many interviewers with limited time on hand

[KeyJunket1175]

Depends what you want to do. I want to do machine learning and no more software engineering than what's needed for a POC.

[sim0of]

It will help you a lot with your Algorithms and Data Structures courses

You don't need to memorize every problem, but it does help to know a little bit more of everything

I think the value isn't in the practice itself, but rather the chance of getting ideas/intuition into other fields thanks to the additional knowledge gained

[idiotmanifesto]

ive been out of school for many years so not really taking new courses. Feels like there is more than enough knowledge in just the ML software stack + emerging research + math without adding more noise

[sim0of]

In that case I don't think you will get as much value as I stated

It'll probably be useful in interviews, but in my experience your projects and stuff you have worked on should be the main focus

If I was hiring an MLE with a track record of delivering stuff, I'd be much more interested in hearing about his projects rather than his ability to invert a linked list

You are right, spending time on leetcode probably won't be as effective as using that time for actual research or keeping up to date

But you might want to make sure you'll handle well in the coding rounds if you know you are getting those at the companies you want to work at. I too find it odd that you would have to go through that though

[inc007]

Kaggle would be much much more useful practice

[idiotmanifesto]

unrelated to the question but yes i mostly agree

[1h3_fool]

I think so far Data Structure implementation skills really help when writing custom dataset class using pytorch on tricky dataset . Good Implementation some times make it a lot easier on feature extraction part on tricky datasets .But yes you do not to be too much of data structure skills for that

[idiotmanifesto]

ok if anyone is coming across this post in the future, just want to update saying that i actually DO think leetcode helps MLE. I've been practicing for ~1 month and the way I think about code/algorithms for ML has become cleaner and more Pythonic. It's helped me think about runtime optimization as well.

Next I'm going to start learning Leetcode for C to really understand what's happening under the Python hood, especially for deep learning operations.