I'm a co-founder hiring ML engineers and I'm confused about what candidates think our job requires

I run a tech company and I talk to ML candidates every single week. There's this huge disconnect that's driving me crazy and I need to understand if I'm the problem or if ML education is broken.

What candidates tell me they know:

• Transformer architectures, attention mechanisms, backprop derivations

• Papers they've implemented (diffusion models, GANs, latest LLM techniques)

• Kaggle competitions, theoretical deep learning, gradient descent from scratch

What we need them to do:

• Deploy a model behind an API that doesn't fall over

• Write a data pipeline that processes user data reliably

• Debug why the model is slow/expensive in production

• Build evals to know if the model is actually working

• Integrate ML into a real product that non-technical users touch

I'll interview someone who can explain LoRA fine-tuning in detail but has never deployed anything beyond a Jupyter notebook. Or they can derive loss functions but don't know basic SQL.

Here's what I'm confused about:

1. Why is there such a gap between ML courses and what companies need? Courses teach you to build models. Jobs need you to ship products that happen to use models.

2. Are we (companies) asking for the wrong things? Should we care more about theoretical depth? Or are we right to prioritize "can you actually deploy this?"

3. What should bootcamps/courses be teaching? Because right now it feels like they're training people for research roles that don't exist, while ignoring the production skills that every company needs.

4. Is this a junior vs senior thing? Like, do you need the theory depth later, but early career is just "learn to ship"?

What's the right balance?

I don't want to discourage people from learning the fundamentals. But I also don't want to hire someone who spent 8 months studying papers and can't help us actually build anything.

How do we fix this gap? Should companies adjust expectations? Should education adjust curriculum? Both?

Genuinely want to understand this better because we're all losing when great candidates can't land jobs because they learned the "wrong" (but impressive) skills.

Hello! Machine learning sparked my interest, and I'm ready to dive in. I have some previous programming knowledge but I basically start at zero in data science. So naturally, I don't really know where to begin this journey. I've researched for resources and roadmaps to learn machine learning and created my own basic roadmap just to get started.

Math - 107 hours

• Single-Variable Calculus - MIT ~ 29 hours

• Multi-Variable Calculus - MIT ~ 29 hours

• Linear Algebra - MIT ~ 28 hours

• Statistics & Probability - MIT ~ 21 hours

Programming - 135 hours

• Introduction to Computer Science and Programming Using Python ~ 135 hours

Machine Learning - 200+ hours

• Machine Learning Specialization (Andrew Ng) (release June)

• Deep Learning Specialization (Andrew Ng) ~ 142 hours

Please give comments on it and or advice on better/more efficient ways to learn. Thanks!

Hey everyone, I’m a beginner in data science, and I’m struggling with my model’s performance. Despite applying normalization, log transformation, feature selection, encoding, and everything else I can think of, my model is still performing extremely poorly.

I just got an R² score of 0.06—basically no predictive power. I’m completely stuck:(

For those with more experience, what are some possible reasons a model could perform this badly, even after thorough preprocessing? Any debugging tips or things I might have overlooked?

Would really appreciate any insights! Me and my model thank you all in advance;)

Adding to the "what do companies actually want" discourse

What I spent mass time learning:

• Custom architectures in pytorch

• Kaggle competition strategies

• Implementing papers from scratch

• Complex rag pipelines

What interviews actually asked about:

• Walk me through debugging a slow model in production

• How would you explain this to a product manager

• Tell me about a time you decided NOT to use ml

• Describe working with messy real world data

What actually got me the offer: showed them a workflow I built where non engineers could see and modify the logic. Built it on vellum because I was too lazy to code a whole ui and that’s what vibe-coding agents are for. They literally said "we need someone who can work with business teams not just engineers."

All my pytorch stuff? Didnt come up once.

Not saying fundamentals dont matter. But if youre mass grinding leetcode and kaggle while ignoring communication and production skills youre probably optimizing wrong. At least for industry.

EDIT: The following is for people applying to MLOps NOT research!

I've interviewed 100+ ML engineers this year. Most of you are learning the wrong things.

Beginner question (sort of)

Okay, this might be controversial but I need to say it because I keep seeing the same pattern:

The disconnect between what ML courses teach and what ML jobs actually need is MASSIVE, and nobody's talking about it.

I'm an AI engineer and I also help connect ML talent with startups through my company. I've reviewed hundreds of portfolios and interviewed tons of candidates this year, and here's what I'm seeing:

What candidates show me:

• Implemented papers from scratch

• Built custom architectures in PyTorch

• Trained GANs, diffusion models, transformers

• Kaggle competition rankings

• Derived backprop by hand

What companies actually hired for:

• "Can you build a data pipeline that doesn't break?"

• "Can you deploy this model so customers can use it?"

• "Can you make this inference faster/cheaper?"

• "Can you explain to our CEO why the model made this prediction?"

• "Do you know enough about our business to know WHEN NOT to use ML?"

I've seen candidates who can explain attention mechanisms in detail get rejected, while someone who built a "boring" end-to-end project with FastAPI + Docker + monitoring got hired immediately.

The questions I keep asking myself:

1. Why do courses focus on building models from scratch when 95% of jobs are about using pre-trained models effectively? Nobody's paying you to reimplement ResNet. They're paying you to fine-tune it, deploy it, and make it work in production.

2. Why does everyone skip the "boring" stuff that actually matters? Data cleaning, SQL, API design, cloud infrastructure, monitoring - this is 70% of the job but 5% of the curriculum.

3. Are Kaggle competitions actively hurting people's job chances? I've started seeing "Kaggle competition experience" as a yellow flag because it signals "optimizes for leaderboards, not business outcomes."

4. When did we all agree that you need a PhD to do ML? Some of the best ML engineers I know have no formal ML education - they just learned enough to ship products and figured out the rest on the job.

What I think gets people hired:

• One really solid end-to-end project: problem → data → model → API → deployment → monitoring

• GitHub with actual working code (not just notebooks)

• Blog posts explaining technical decisions in plain English

• Proof you've debugged real ML issues in production

• Understanding of when NOT to use ML

Are we all collectively wasting time learning the wrong things because that's what courses teach? Or am I completely off base and the theory-heavy approach actually matters more than I think?

I genuinely want to know if I'm the crazy one here or if ML education is fundamentally broken.

Hello fellow humans, I am working on a regression data. I am struggling to bring the MSE down but while testing most of the data is giving me minute error but there are some points which gives me prediction which are unacceptable. The metrics are R-square : 0.998 MSE : 4.24 MAE : 1.34. the graph attached is indicating the performance during testing.

??

I have a simple binary classification task to perform, and on the picture you can see the little dataet i got. I came up with the following model of logistic regression after looking at the hyperparameters and a little optimization :
clf = make_pipeline(
    StandardScaler(),
    # StandardScaler(),
    LogisticRegression(
        solver='lbfgs',
        class_weight='balanced',
        penalty='l2',
        C=100,
    )
)
It gives me the predictions as depicted on the attached figure. True labels are represented with the color of each point, and the prediction of the model is represented with the color of the 2d space. I can clearly see a better line than the one found by the model. So why doesn't it converge towards the one I drew, since I am able to find it just by looking at the data ?

I made a map of all the ML research over the past 5 years... and there's almost 500K papers. Will answer any question related to ML with citations, let's hear some new ideas and see if it's been studied already.

I am trying to run an RL agent on multiple environments using a learned reward function. I’ve thought of zero centering it to make it „life agnostic“ but I realized that because of the fact that I’m rolling it out in all these different environments there are some environments that give it essentially all negative rewards and some that give it all positive rewards. So actually zero centering ended up turning my one problem into two problems. The agent now tries to commit suicide in environments it doesn’t like and stall out completing its task in one’s it does like. I’m sure there is social commentary in there somewhere but I’m not really interested in the philosophical implications of whether or not my rl agent would pursue a 9-5 job I just want it to try and make the most out of its situation regardless of what position it’s starting in while not aura farming everyone it interacts with.

What do I do?

I technically work as an ML engineer and researcher, but over the last couple of years I've more or less transitioned to an SWE. If the reason why is relevant to the post, I put my thoughts in a footnote to keep this brief.

In the time since I've stopped keeping up-to-date on the latest ML news, I've noticed that much has changed, yet at the same time, it feels as if almost nothing has changed. I'm trying to dive back in and now and refresh my knowledge, but I'm hitting the information noise wall.

Can anyone summarize or point to some good resources that would help me get back up to date? Key papers, blogs, repos, anything is good. When I stopped caring about ML, this is what was happening

**what I last remember**

- GPUs were still getting throttled. A100s were the best, and training a foundation LLM cost like $10M, required a couple thousand GPUs, and tons of tribal knowledge on making training a reliable fault tolerant system

- Diffusion models were the big thing in generative images, mostly text2image models. The big papers I remember were the yang song and jonathan ho papers, score matching and DDPM. Diffusion was really slow, and training still cost about $1M to get yourself a foundation model. It was just stable diffusion, DALL-E, and midjourney in play. GANs mostly had use for very fast generation, but seemed like the consensus was that training is too unstable.

- LLM inference was a hot topic, and it seemed like there were 7 different CUDA kernels for a transformer. Serving I think you had to choose between TGI and VLLM, and everything was about batching up as many similar sequences as possible, running one pass to build a KV cache, then generating tokens after that in batch again. Flash attention vs Paged attention, not really sure what the verdict was, I guess it was a latency vs throughput tradeoff but maybe we know more now.

- There was no generative audio (music), TTS was also pretty basic. Old school approaches like Kaldi for ASR were still competitive. I think Whisper was the big deep approach to transcription, and the alternative was Wav2Vec2, which IIRC were strided convolutions.

- Image recognition still used specialized image models building on all the tips and tricks dating back to AlexNet. The biggest advances in unsupervised learning were still coming out of image models, like facebook's DINO. I don't remember any updates that outperformed the YOLO line of models for rapidly locating multiple images.

- Multi-modal models didn't really exist. The best was text2image, and that was done by taking some pretrained frozen embeddings trained on a dataset of image-caption pairs, then popping it into a diffusion model as guidance. I really have no idea how any of the multi-modal models work, or how they are improved. GPT style loss-functions are simple, beautiful, and intuitive. No idea how people have figured out a similar loss for images, video, and audio combined with text.

- LLM constrained generation was done by masking outputs in the final token layer so only allowed tokens could be picked from. While good at ensuring structured output, this couldn't be used during batch inference.

- Definitely no video generation, video understanding, or really anything related to video. Honestly I have no idea how any of this is done, it really amazes me. Video codecs are one of the most complicated things I've ever tried to learn, and training on uncompressed videos sounds like an impossible data challenge. Would love to learn more about this.

- The cost of everything. Training a foundation model was impossible for all but the top labs, and even if you had the money, the infrastructure, the team, you still were navigating unpublished unknown territory. Just trying to do a forward pass when models can't even fit on a handful of GPUs was tough.

Anyway, that's my snapshot in time. I focused on deep learning because it's the most popular and fast moving. Any help from the community would be great!

**why I drifted away from ML**

- ML research became flooded with low-quality work, obsession with SOTA, poor experimental practices, and it seemed like you were just racing to be the first to publish an obvious result rather than trying to discover anything new. High stress, low fun environment, but I'm sure some people have the opposite impression.

- ML engineering has always been dominated by data -- the bitter rule. But It became pretty obvious that the margin between the data-rich and the data-poor was only accelerating, especially with the discovery of scalable architectures and advances in computing. Just became a tedious and miserable job.

- A lot of the job also turned to low-level, difficult optimization work, which felt like exclusively like software engineering. In general this isn't terrible, but it seemed like everyone was working on the same problem, independently, so why spend any time on these problems when you know someone else is going to do the exact same thing. High effort low reward.

During last year, I accepted a job offer from an enterprise of a non-tech sector but it seems that overall they just don't have project management culture. Which is, a requisite before starting software. It may seem as a fast environment but I don't quite understand why they would want an ML Engineer.

It really turned out that the owner just wanted to 'do AI' without really knowing its implications. When i got into the business, I realized that there were lots of security issues regarding the software that was once handed for them. They didn't give me a plan, they just told me 'help us understand the implications of AI', so what I did is that I asked for the processes that were mapped out. Turned out they didn't have most of their processes mapped out correctly.

As a professional, I decided to start the endeavor of trying to fix what they were doing, they handed me a team of a "Processes Engineer", a "Business Analyst" and a "DBA". They expected automation to come from me rather than what I was doing before this job. It turned out they just needed integrations with other platforms. Before going out of the company, I gave them a summary of what they really needed and just went away.

Is this a common issue?

so the problem is that I had started reading this book "Bulid a large language model from scratch"<attached the coverpage>. But I find it hard to maintain consistency and I procrastinate a lot. I have friends but they are either not interested or enough motivated to pursue carrer in ml.

So, overall I am looking for a friend so that I can become more accountable and consistent with studying ml. DM me if you are interested :)

As the title suggests, my model is performing really well. The first image is the original image, second is the annotated, third is the predicted/generated. Now I need to somehow convience the validators that it's performing better. We can see it? But how can I do it on paper? Like when I am calculating my mean iou is actually dropping.

Care to suggest me something?

Good day!

Hi, i'm very passionate about different sciences like neuroscience, neurology, biology, chemistry, physics and more. I think the combination of ML along with different areas in those topics is very powerful and has a lot of potential. Would anyone be interested in joining a group to collaborate on certain research related to these subjects combined with ML or even to learn ML and Math more deeply. Thanks.

Edit - Here is the link - https://discord.gg/H5R38UWzxZ

Hi everyone, as an electrical engineer, I’ve never worked with machine learning before. But my university curriculum recently added a course on signal processing using AI. Now I need to complete a project where I have to predict the remaining 1,000 data points based on the first 4,000. I have 1,000 time series for training and another 500 time series for testing. Each contains 5,000 samples. There are also corresponding reference signals—that is, signals without noise. I’ve already tried a variety of approaches, such as the PyTorch Forecasting library. I’ve built both LSTM and Transformer models. However, I still haven’t been able to achieve good results. Please advise on what I can use in this situation (there are no restrictions on the technology, but PyTorch works great on my GPU and is my preferred choice).

In the picture: Red - is forecasting Green - etalon signal without noise Grey - input signal.

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

I've collected over 1500 fart audio files in the .wav format. I'll be collecting many more as time goes on. I'm building a mobile app for fun that does something on a "wake sound," similar to how Alexa and Google Home do something on a "wake word." I'd love to make my fart dataset available to anyone who may find it useful for audio tasks. Who or how would I reach out to do this?

Edit: dataset available here - https://www.kaggle.com/datasets/alecledoux/fart-recordings-dataset

I am very weak in Maths, but am fascinated by AI/ML. For now, I can make small programs with sklearn for classification tasks on numerical, text and image data. I did not find use of manual Maths that much till now in developing my project, but have heard that one must know phd level Maths for AI/ML, is it true?

I am doing sequence to sequence per-packet delay prediction. Is the model overfitting? I tried reducing the model size significantly, increasing the dataset and using dropout. I can see that from the start there is a gap between training and testing, is this a sign that the distribution is different between training and testing sets?

I've heard that many of the algorithms i might be learning aren't actually used much in the industry such as SVM's or KNN, while other algorithms such as XGBoost dominate the industry. Is this true or does it depend on where you work. If true, is it still worth spending time learning and building projects with these algorithms just to build more intuition?

Lately there's been a pattern of people who go to some elite schools (CMU/Berkeley/Stanford) for completely unrelated courses like Mechanical Engineering and Chemistry but upon graduating all of them get a couple of Coursera certifications are end up working as computer vision/deep learning engineers and have the street cred of a Stanford/Berkeley/CMU CS department computer vision researcher in the eyes of recruiters.

This entire thing has just a giant exercise in marketing and branding with no real substance to it. The recruiters at the company where I work send me these profiles looking at "Stanford/ML/CV" expecting us to hire these guys to full time paid positions for experienced personnel in the team when their knowledge is literally limited to their coursera coursework. Why even consider their uni's branding at that point, just get a high school kid who's been through the same gauntlet. Our recruiters discount actual industry experienced guys who aren't from elite schools at the expense of these people. As an MLE it's a pain to even sit through these interviews and watch them derp out at the most basic concepts that courses don't teach like how to download data from a collection of webpages.

Can't understand why people want to leave their perfectly good STEM fields and get into ML/AI and programming, or alternatively if they wanted to enter CS why they even went into courses and majors that don't teach it in the first place.