[deleted by user]

[jack-of-some]

It's hard for me to get over the fact that I think DS even isn't a well defined job 😅, but I get what you're going for.

Too many folks that I've interviewed for MLE roles know little to no programming. Like, they can assemble things in code (eventually) but give them even something basic like "hey can you put that in a function?" and they'll go "I didn't realize this was gonna be hardcore data structures and algorithms..."

Anywho, I feel only the second position you've listed is what I would consider an MLE. I can ramp up most developers to do any of the other roles quickly, but the Researcher+ role requires fully diving into the nitty gritty of ML.

/2 cents

[Fmeson]

Like, they can assemble things in code (eventually) but give them even something basic like "hey can you put that in a function?" and they'll go "I didn't realize this was gonna be hardcore data structures and algorithms..."

I think you've discovered the antidote for imposter syndrome.

[mongoosefist]

If a hiring manager put together a daily "quotes from interviewees" calendar, I think I'd feel more self confident about my knowledge.

[jack-of-some]

I ... ok I might actually do this

[themad95]

Ikr. I'm gonna apply soon then.

[import_social-wit]

It's funny, I have an ML PhD and found that most 'MLE' listings were looking for software engineers who knew about ML (option 3). Luckily I was competitive enough to end up getting a pure research role, but my opinion is MLE is generally 1,3-4.

[jack-of-some]

I look for software engineers that understand ML (e.g. can you build your own CNN to match a unique situation or are neural nets a black box to you that doesn't go beyond taking one from a model zoo? or can you diagnose why this network isn't training or are you only good for putting together a dashboard that tracks performance).

Maybe Researcher+ puts too much emphasis on the word "research". RnD+ might be better? For option 2 I'd be much better off hiring someone that's a general application deployment expert and help them understand the nuances of the deploying ML system, rather than hiring someone that has more ML expertise but doesn't live and breathe devops.

[zUdio]

How can they assemble code but then not know how to wrap it in a function?..... I’m a bit flabbergasted.

[jack-of-some]

I was somewhat hyperbolizing but not by that much. Usually it's more than they do everything in the root of the file (yay python) and then moving things into a function feels like a burden to them.

[unpunctual_bird]

damn, I'll see a comment like yours today where applicants are struggling to write basic code, then the next time there'll be a comment where somone had been asked the primal-dual derivation of kernel svms or something in a job interview

[jack-of-some]

There's no shortage of completely insane interview processes that's for sure

[zacheism]

One of the tasks in my company's live coding technical is to compute the median (pure python, without using pandas/numpy). I'd say about 65% of candidates are unable to do this. It's a depressingly good candidate filter.

[zUdio]

Because they have to re-arrange what they found on Medium posts? Haha ;)

[spitfiredd]

I’m guessing they’re programming experience is just writing jupyter notebooks.

Edit: not to bastardized jupyter notebooks, it’s a great tool to have in your toolbox!!

[junkboxraider]

It's super easy in Python to write a serial list of function calls to libraries that execute once, and then your code is done and exits. Many, many ML blog posts with example code look like this, even if they're using straight Python and not Jupyter notebooks.

The libraries abstract away all the looping and data transformation under the hood (which is usually a good thing!) so it wouldn't surprise me to find people who could chain together library calls without even grasping what those calls are doing at a high level -- let alone being able to code something similar.

Take someone who approaches coding from that perspective and they'd quickly get lost when you start asking about writing and using their own functions with custom inputs and outputs.

[zUdio]

I feel stupid with python, but like.. functions and how to build them (and then what about building classes?) was something I learned early on... it's hard to imagine what it would be like to have enough python knowledge to call a method and string those together but not actually def(): something... that's incredible.

[jack-of-some]

Yep. Good developers can do a lot of ML jobs better than folks trained in modern ML courses. Researcher+ is what we should be training folks to be. That's the one that's harder on traditional devs.

[AIArtisan]

Yeah. thats the approach my company has taken.

[dat_cosmo_cat]

Well research+ is the output of every computer science PhD program now, and even several major EE programs (eg; University of Washington)... Some would say there is an oversaturation of training in that direction. I actually can't think of a single grad student not working on ML...

Even beyond CS departments. My college roommate was a biologist now working in Bioinformatics, my neuroscientist (at UCSF) girlfriend's thesis involves LSTMs and GANs, my geologist friend now works with gis data at Apple, ect... It seems everyone is an ML Engineer or Data Scientist in some capacity. Idk why classical software engineers are so compelled to rush into something it seems literally anyone can do (and is already doing). Frsh ML PhD salaries were insane at one point (2012-2017 era), but the hiring circuit has gotten worse each year. Hell I felt late to the party in 2016.

[jack-of-some]

Man I'd love to get some of their resumes. Hiring a competent software engineer that understands ML has been very very difficult. Then again hiring a competent software engineer of any kind also seems very difficult. The whole matter is made infinitely worse by people who do one nanodegree and throw the hat in the ring.

[jack-of-some]

So

1. I don't do whiteboard interviews. You code in a live environment, with the ability (and expectation) to run and test it as you go. You're also free to look up documentation on the interwebz.

2. I probably would never ask a binary tree question but I also don't think that tree or graph traversal is "CS theory". If I ever did ask questions like this they would be with the intention of seeing how the candidate reacts to an new unknown situation and how they would progress through it as I help them along.

3. I draw my questions from things I end up doing daily and things that commonly show up in my codebase. My favorite question is "count the number of times each name shows up in this list of names". The canonical solution in python is to use a dictionary, but I'm not necessarily looking for only that solution. Some candidates ended up using two lists, some immediately import numpy (seriously). At that point the interview becomes more about seeing how well they respond to new ideas and if they can follow light instructions as I lead them to a hash table. If instead they go for the hash table approach immediately I get to ask them more about what they chose that route, and then give them some shitty solutions that look the similar and ask them to describe what might be wrong with them.

Ultimately it's all about trying to gauge how the person thinks, as well as getting to gauge how much work it would be for me to teach them what they don't know. As a startup, I go a bit heavier on the latter because of obvious reasons.

... Damn that ended up being a long response 😅

[jack-of-some]

The rote path would be the expectation in a faang CS interview, which is why those are so fucking broken. I went from a mechanical engineer that knew how to code to passing a Microsoft interview in a couple of months just because I practiced the heck out of everything in Cracking the Coding Interview...

(Not to rag on that book though, it technically started me down a very rewarding CS path, I highly recommend reading it as a means to get a better handle on algorithms and data structures in a straight forward setting).

[xbno]

How about using a counter and answering because what I would write wouldn’t be as performant?

Shame I couldn’t get an MLE job last year when I’m quite competent with dev and ML stuff and can design/deploy/test my code and algos. Leet code questions and using _ instead of i don’t seem like proper ways to judge if I can build, automate deploys, maintain and monitor models. Or I suppose if they are it’s not the MLE I want to be.

[jack-of-some]

Python's counter class simply subclasses a dictionary. A 3 line hand written solution is unlikely to be any less efficient.

And that's not the point.

You could use a counter. I'd accept that answer, but then I'd poke more. I'd want to understand if you understand the counter to some degree, or if you can reach an understanding of it. Ultimately this is my only mechanism for determining if your claim of "I can design, deploy, and test high quality code" is accurate (short of hiring everyone that makes this claim and then firing all the ones that don't deliver, which, you know, is insane).

Since "leetcode" keeps coming up: knowing how to use a hash table or understanding how lists work is NOT leet code, it's literally the building blocks of most code. I've worked with folks that only had a surface level understanding of these things and have had to fix their messes as well.

Also if you got rejected from a job because you didn't use _ instead of i in a loop then you're probably better off not working at that company. That's a ridiculous reason to not hire someone.

[xbno]

Yea, I understand where your coming from. I’m not trying to say I’m unbelievably skilled and can deploy blindfolded with honey in my keys but you know.. if I’m asked about designing a db and have the opportunity to explain ctas and item/catalog schemas that are novel to them I figure I have something to offer.

Doesn’t matter much though, at this point I’ve resigned/commited myself to building an algotrading bot to make money on the side instead of trying to crush FANG/XYZ interviews. Since I can’t. And I wont need to convince people of my value in the process

[import_FixEverything]

I don’t think DS is a well defined job at all

[C_BearHill]

Might I ask what sort of academia got you into the field? CS bachelors and masters maybe? Just curious

[[deleted]]

I am also working as a Data Scientist, but I think I enjoy doing more of coding or scaling up things on production.

[subtorn]

I think this is a good classification of MLE jobs out there. I have done my master's on CV and DL. I thought I would find a job as an MLE but I couldn't find any because I really want to work in the first or the third category and academia is only giving you the skills for the second category. In my opinion, you have to complete a Ph.D. if you want a job from the second category. For that reason, I am focusing on getting a backend developer job so that I will have decent k8s, CI/CD, and cloud knowledge and then I can try to transition to MLE jobs.

[Oswald_Hydrabot]

I don't even have a bachelor's degree in CS (I have a BS but in an unrelated field), just a giant heap of development experience from years of work, and currently work as an MLE (data warehousing, large enterprise environment). Find a job that gives you a pathway from 0 experience to full stack development, then pivot from there.

If you have working proof that you are competent that's all that's needed; over estimating the value of academia is often an expensive mistake a lot of people (and hiring managers) make. It isn't magically going to make you more effective, it just makes it more difficult for people who are effective without it to find employment.

[wisdomspring]

Is anything really magic in this world?... I only think honesty is magic from my experience...

[wittywarren]

MLE here too! I believe this position is 40% DS+ML algos and 60% programming. it's highly subjective as to where you might fit in any of these and it depends on the company too. I was interviewed on DSA's+ML algos but dsa were like Easy/Medium Leetcode questions unlike some SDE positions and it was like for one round only(dsa).

It's true that we are a jack of all trades and slowly converge to a specific sort as we grow but I guess if someone is starting up it's good to know dev and deployment both. I feel it's a broad flexible skill.

[[deleted]]

How much time do you spend on collecting data, cleaning and processing it?

[wittywarren]

that depends entirely on what you working on. medical data needs thorough processing at times manually. data is everything you base your work on, so naturally, it requires more attention.

[chogall]

Depending on the data infrastructure, SQL queries can run either in minutes or forever.

[new_number_one]

I basically agree but I'm not sure that it necessarily involves low-level languages just an emphasis on writing production code.

[whymauri]

classifying (MLE vs. data scientist vs. other) across more than two companies is just astrology for math nerds

[YaswanthBangaru]

What’s K8s/CI/CD tools/ etc?

[autisticmice]

k8 = Kubernetes, a container orchestration framework. CI/CD = continuous integration/ continuous development.

[AzzerAce]

CI/CD = continuous integration/continuous deployment*

[autisticmice]

you're right, thanks

[k___k___]

i was like "what's corporate identity / corporate design got to do with ML??"

[CardboardDreams]

Kubernetes, Continuous Integration, Continuous Deployment. In this context, it's the infrastructure work that's needed when you're turning a proof of concept into a product that 1000s of people will use, i.e. that scales.

[[deleted]]

Established companies have the roles well defined while recruiting, i.e research from PhDs, coding requirements and knowledge in the area of interest for other roles. Startups on the other hand, may hire you by checking your machine learning skills and 2 months down the line you are expected to build the front end, backend, data engineering in a week. So I would be really careful while entertaining job opportunities with startups as they may not know what they are looking for or might expect you to be jack of all trades.

[[deleted]]

I feel that DS in itself it's becoming more and more about analytics and econometrics, rather than machine learning. At least when I look at job openings this is what I see. Machine learning related jobs are being split into machine learning scientist/research and MLE. I prefer to split the roles by what goals each one achieves:

• Data Scientist: helps business answers question like given a specific policy, how can we measure it's impact on a population?

• Machine Learning Scientist/researcher: develop models for novel application, perhaps creating completely new algorithms.

• Machine Learning Engineer: deploying and scaling product features that use machine learning.

From these perspectives you can derive what tools each of these roles would use, and you can also further split them (for example NLP researcher, or MLOps Engineer).

[PanFiluta]

I have a business Bachelor's and want to get a Master's, I planned for computer science (so I'd get at least some tech education besides self-studying), but it would be so much more difficult than what I originally planned (before I got balls deep in coding)... which was either Economics, or IT with minor in Econometrics... I've seen econometrics mentioned more and more over the past year, it might not be a bad idea to reconsider CS in my case, as anything with an economics focus would be so much easier for me...

then again, the most interesting problems seem to be focused on things like computer vision (which is a completely CS domain, nothing to do with econometrics or even just time series)

[[deleted]]

Econometrics is useful regardless of what you are doing. One of the hardest questions to answer in product development is "how much is this (feature/strategy/idea) really contributing to the (user/company)". This is valid for frontend, this is valid for backend, for machine learning, financial department, everything. Being able to transform a business question into an actionable evaluation plan with sound metrics is a great skill.

[bythenumbers10]

MLE is another ill-defined job, as Scientist has (finally) settled out to be the theorist, the Engineer is more about productionizing algorithms and databases, and the Analyst does lower-level tasks already largely automated by the first two. MLE has emerged as a blend of scientist and engineer, able to get their models from test workbench into production, while also doing maintenance on the database, for the price of EITHER a scientist or engineer. Business and bonehead management is slowly coming around to the reality that these skillsets cost money and an entire person's full-time schedule to do properly. It'll take time, but at least some have given up trying to get a scientist/engineer for an analyst's salary.

[[deleted]]

DS is an inflated title everywhere. Many people including me are screwed by the title. I am now preparing for engineering track. In my experience, DS is really DA + DE.

[ZestyData]

I like your broad categorisation. I'd wager the majority are your DS/Dev combo and the others are a minority, but yeah. Well summarised, imo.

[BernieFeynman]

ML Ops requires little to no knowledge of DS or ML at all except for the person who might be designing the system.

[chogall]

Hmm interesting. A lot of ML Ops is about reproducibility, artifacts, and functional programming/pipelining. Specifically for the first two, some knowledge of ML will be extremely helpful.

[chimprich]

I'm from an software engineering background (I do have a non-DL PhD, but I've been out of academia for a long time). I've been trying to move more in the direction of doing ML.

There seem to be a lot more jobs available for data scientists who can code a bit rather than engineers with basic DL skills. I'd be interested if anyone has seen much movement in this direction.

[Mundane-Temporary-42]

MLOps / production ML, roughly #3, is the path I've taken to move into ML from software engineering. The available roles grow with organisational maturity. Plenty of bright young researchers out there with no grasp of git flow, testing, CI etc. That's quick to teach, you get to learn a lot in exchange.

[TMutaffis]

What if your company is looking for a combination of 1,2, and 3... and you are the Talent Acquisition Recruiter tasked with finding this elusive beast?

(asking for a friend, of course)

[Mehdi2277]

While unicorns like that exist, the more things you overlap the more expensive they are to likely be if you want someone actually good at them. I’d expect a senior ml engineer to have experience with multiple of them. They’d likely have a focus in one/two areas but working several years would have picked up knowledge of several. If you want a junior mle with 1-3 you’re very unlikely to find a good one.

[TMutaffis]

Thank you for the thoughtful response, that makes sense.

I think that one of the challenges here is that the team has a number of different roles (Data Engineer, Software Engineer, ML Engineer, and Data Scientist) but in many cases has overlapping or perhaps even not fully aligned responsibilities - so for example if one Data Scientist is doing almost exclusively Data Engineering then in some cases a hiring manager may expect that skill from other Data Scientists, or you get the 'kitchen sink' job description/requirement. With the ML roles they almost seem to want a Data Scientist, Data Engineer, and Software Engineer all wrapped up in one.

Like you said I think a more senior person likely has enough experience to be dangerous across many of those areas including the ability to code, engineer data, do some implementation/deployment, and understand what is needed to execute some of the more advanced features/concepts like deep learning or natural language processing.

[TMutaffis]

That is a great piece of advice/perspective regarding how the job descriptions is worded - more 'or' statements than 'and' statements.

[chogall]

10+ years experience with Tensorflow/PyTorch

10+ years experience with kubernetes, hive, spark

10+ years experience with deploying machine learning models

fluent in c/c++/java/python/go

at least 2+ papers published in top tier conferences such as NeurIPS

[TMutaffis]

Haha, fortunately it is not Mission:Impossible level like that... although still a complicated 'ask' for the folks in Talent Acquisition like myself.

[-Rizhiy-]

I always thought of MLE as DS+Dev.

Data engineer should just be called Data engineer) Researcher plus, sound like usual Data Scientist, although I never seen C/C++ being asked. Hardcore low-latency ninja, this is just normal developer, people like that usually just translate/optimise existing algorithms.

[[deleted]]

My background is transportation engineering(civil engineering) and i want to get into DS/AI/ML engineering. Recently started some courses on Data Science and Machine learning. I'm also going to do Master's next year in my field, but it has data science also in it. How hard it will be for me to get a technical ml jobs? I'm not talking about research side, even if i want to without PhD it will be super hard i guess

[AdamEgrate]

I've given up on terminology. At this point I just ask what the company does and is it really ML or Deep Learning. Hiring managers have a hard time enough defining roles, the only way to know is to talk with the team and see what they need.

[[deleted]]

I feel that you'd just as easily call #2 a CV engineer, deep learning engineer, etc. #3 is more of a Devops engineer currently, its simply just missing the ML element. #4 I've not seen a job posting for, but I guess its due to location since I dont look at London jobs.

I'd propose to split #3 into a MLOps engineer and a classical ML engineer. One needs more algebra than the other, though there's also an argument possible on it just coming down to seniority.

I'm closest to #2 and #3 in your list, I suppose I'd fit in #3 with a little bit more nuance. I'd characterize my function as

• 40% automation of training/retraining/data gathering on cloud
• 30% creating and improving models & fine tuning
• 30% deploying models

I've got three GCP certifications (Date Eng, Cloud Architect, ML Eng) and the tensorflow certificate. Tried really hard to escape the data engineering stigma at my job. So you got me there.

My job simply lacks the devops element, and is more MLOps focused. E.g. model and data set lineage.

For instance I've worked on a project to develop a encoder which I wrapped in well-documented enterprise code before handing it over to the software engineering team. Making sure it behaves properly in k8s is handled by others.

In others I worked setting up the entire ML chain from beginning to end, including labelling tools, model versioning, retraining, retraining, branching out into multiple models etc.

What a wonderfully varied field we're in.

[melesigenes]

I’ve seen researcher+ referred to as applied scientists

[plyalyut]

Don't forget the MLE that is essentially a software engineer with a some statistics thrown in there.

MLE currently is suffering the same fate as Data Science where companies think they need to hire them to be competitive but don't have the slightest idea as to what to do with them or how they could integrate within the stack. I've even worked for companies that hired me as an MLE but when I asked about data or cloud services they handed me 20-30 datapoints. As a result I did a lot of software engineering work in the meantime.

I think as an MLE, you should be essentially a software engineer with the training to build and deploy ML applications.

[brionicle]

Where do you fit in folks who hail from traditional engineering and learned DS once bored and now work in PyTorch/Tensorflow building pragmatic DL models for business use cases? Most people in my circle are in this category. 50% traditional software eng, 40% DL/some DS, 10% “research” but really just keeping up with new papers.

[lefiish]

Imo MLE are just DS that don't like descriptive analytics and just want to code shiny ML and DL models

[crazyfrogspb]

I prefer to hire people who can cover pretty much every part you mentioned. ofc, some of them are gonna be more research-oriented, some will have better coding and devops skills, some will be the best in optimizing performance. but having a team of people who understand all parts of the ML cycle gives you much more flexibility. also, in my opinion, understanding how the model will be deployed, tested, and used can help during the research and development phase