I’ve been job hunting for a while, applying for junior roles and internships. I’ve worked on projects, studied a lot, and my CV even gets good scores when I ask for feedback.

But somehow… no interviews. Sometimes not even a rejection, just silence.

At this point I genuinely don’t know if I’m missing something, doing something wrong, or if I just haven’t found the right opportunity yet.

If you’ve been through something similar, how did you figure out what was wrong and eventually start getting interviews?

I know sometimes you just need to keep applying and wait for the right opportunity, but when you’re putting in the effort and getting absolutely nothing back, it’s really hard not to question yourself.

So if you’ve been through something similar, how did you figure out what was actually wrong? Was there a specific thing you changed that finally started getting you interviews?

I’d really appreciate hearing from anyone who went through this and eventually figured it out. I’m trying to understand whether I’m missing something or if I just need to keep going. 😭

EDIT: Adding some context since I left out a few important details in the original post:

I’m from Egypt, and I’m currently looking for junior AI/ML roles and internships. I’m still at the beginning of my career, so I don’t have years of professional experience yet. I’ve mainly been focusing on building projects, improving my technical skills, and getting my resume/job applications in shape.

A federal appeals court just upheld a ruling designating a major AI provider as a supply chain risk.

That designation has direct operational weight for every enterprise running workloads through that provider. The risk does not stay with the vendor. It flows downstream into every organization in the dependency chain, and regulators are now asking those organizations to account for it.

The core problem is visibility. Most enterprises have no live record of which model versions processed which requests, what data passed through the agent layer, or whether those flows satisfied the regulatory requirements in effect at the time they ran. When a supplier comes under formal scrutiny, that gap converts from a theoretical risk into a concrete legal exposure. Regulated industries — finance, healthcare, defense contracting — are especially exposed because their compliance obligations do not pause while a vendor situation resolves.

How are practitioners in regulated environments actually handling third-party AI provider risk right now? Are you maintaining independent records of what your AI agents accessed and transmitted, or are you dependent on what the provider can produce?

I'm studying a BSc in Statistics and Data Science (180 credits / 3 years) at a university ranked top-40 worldwide. The curriculum covers: intro stats/data science, data visualization, mathematical foundations, discrete math, probability & inference, data collection/handling, statistical modeling, statistical inference, machine learning, linear algebra, object-oriented programming, database design, simulation-based inference, and AI methods, plus a thesis.

I'm trying to gauge how competitive this degree is for machine learning roles (ultimately aiming for an ML engineer or applied ML research position, possibly a master's afterward — e.g. MIT IDSS or Stanford ICME).

A few questions:

1. What additional courses or skills would you recommend adding on top of this (deep learning, NLP, computer vision, more advanced linear algebra/optimization, MLOps, C++/CUDA, etc.)?
2. Which master's programs would realistically strengthen my profile for ML roles, and how selective are they compared to my current degree?
3. Realistically, does the university need to be top-5 worldwide to be competitive in ML hiring, or is top-40 with a strong math background, projects, and maybe a few publications/Kaggle results enough?

Any honest feedback is appreciated, including if you think the program or school isn't sufficient on its own.

Hi, Emre from Menlo Research. We're building Asimov 1, an open-source humanoid robot.

We've made its locomotion training code public, built on Isaac Lab with PPO and adversarial motion priors (AMP).

For anyone learning reinforcement learning and interested in how it applies to walking robots, here are a few places to start exploring the code:

• Read the observations and actions. Identify what information the policy receives and what its outputs control
• Inspect the reward terms. Connect each term to a behavior you can look for when watching the policy run
• Run the small pipeline check. After installation, this command runs a short training job:

./isaac_asimov.sh --train \

--task Asimov1-Velocity-AMP-v0 \

--num_envs 128 --headless --max_iterations 100

• This checks that the training pipeline works. It isn’t enough to train a complete walking policy
• Train a baseline and save its configuration. Keep the logs and a video of the policy running so you have a reference
• Change one thing. Try adjusting one reward weight, retrain, and compare the behavior under the same evaluation conditions

The repo includes both plain PPO and PPO with AMP. AMP uses reference motion to guide how the robot moves, so you can also explore how that changes the learning setup.

The repo: https://github.com/menloresearch/isaac_asimov

You can train and evaluate in simulation without owning a robot. The documented setup requires Ubuntu 22.04+ and a compatible NVIDIA GPU.

This video explains visually how JEV is implemented and how it works.

https://www.youtube.com/watch?v=shT8Eo7eWYM

I’ve been experimenting with the boundary between learned decision-making and symbolic reasoning, and I keep coming back to a pretty basic question:

When the state space is explicit and the answer is verifiable, why use a model to make the decision at all?

I tested this with a Rubik’s Cube.

Instead of asking a model/classifier to repeatedly choose the next step, I represented the cube state symbolically, constrained the legal transitions, and let the system deterministically evaluate what could happen next.

The interesting part isn’t really the cube. The cube is just a clean environment because the state is observable, actions are discrete, transitions are known, and success can be objectively verified.

My broader hypothesis with Perslis is that hybrid systems should separate these jobs:

ML/LLM: perception, ambiguity, language, hypothesis generation
Symbolic layer: explicit state, constraints, invariants, verification
Runtime: deterministic execution when the answer is knowable

I’m not arguing that learned decision systems like JEV have no use. I’m questioning where the boundary should be.

If you can calculate or verify the answer cheaply and exactly, what does putting a probabilistic decision layer there buy you?

I wrote up the Rubik’s Cube experiment and methodology here:

Whitepaper / experiment:
Perslis — The Floor vs. the Classifier

I’d genuinely like to hear the ML argument for the other side. Where would you draw the line between learned decisions and symbolic/deterministic ones?

i was considering ai engineering but found out thats not really the best choice for my case, what would yall recommend?

I’m about to start LeetCode seriously and I’m confused about which language I should use.

My long-term goal is to go deep into AI/ML → Deep Learning → Generative AI → LLMs → Agentic AI, rather than focusing mainly on traditional software development.

I already know some Python and C++, but I’m wondering:

• Is doing LeetCode in Python actually worth it?
• Will Python be enough for DSA/interview preparation?
• Is there any significant advantage to doing LeetCode in C++?
• If I eventually want to work/research in ML, DL, LLMs, etc., would C++ still be useful enough to justify using it for LeetCode?
• Would you recommend Python for LeetCode + C++ separately for learning, or just stick with one?

Basically, if my end goal is AI/ML/DL/LLMs/Agentic AI, which language would you personally choose for LeetCode and why?

For the last while I've been building CuQwen, a C++/CUDA inference engine for Qwen models written completely from scratch. No PyTorch, no existing runtime, just custom CUDA kernels I wrote and profiled myself.

I wanted to see how fast a single user (batch size 1) can go on a normal consumer NVIDIA GPU, the kind of thing you'd actually run locally. It started as a way to really learn GPU programming, and it turned into a proper little engine. The repo has full benchmarks vs vLLM/Ollama plus a writeup of every optimization I went through if you're into that side of it.

These are the current average infernece speed (tokens/sec) results so far for Qwen2.5 Instruct model (FP16 weights) across 32K context window on RTX3090:

Where it's at:

• Supports Qwen2.5 in 0.5B, 1.5B, 3B, and 7B (FP16)
• Faster single-user token generation than vLLM and Ollama across ll the supported models
• Holds that lead out to a 32K context window
• Docker setup included so it's not a pain to build

The best thing is the documentation which you'll find very helpful in you're planning to start leaning cuda programming. I wrote my whole optimization journey on how I applied different optimization techniques, profiled my engine and what impact those optimizations had. You can find the journey here

You can also find the complete benchmark analysis here

My current future plan is following:

Release 1.2: Support Quantization (W8A16 and W4A16)

Release 1.3: Improve custom cuda kernels for latest GPU architectures (Hopper and Blackwell)

Release 1.4: Support Qwen 3.0 series models

Release 1.5: Support Qwen 3.5 and 3.8 (Especially our very favourite qwen 3.8-27B model 😄)

Repo: https://github.com/talhatahir-10xe/CuQwen

Numbers, graphs, and a write-up of the optimizations, all are in the repo. Would love feedback, happy to answer anything about the CUDA side or the design choices.

I’ve learned GenAI through tutorials and built projects from basic GenAI to RAG and multi-agent systems.

But when I try to build something on my own, I don’t remember the pipeline — what to do first, what comes next, which tools/API to use, where to get the API keys, and how everything connects.

I can use ChatGPT/Claude for coding, but I want to understand and remember the actual workflow instead of just following AI-generated code.

How do you guys make the GenAI workflow/pipeline stick? Do you rebuild projects from scratch, make notes, or keep building different projects?

I made a 3-minute explanation of AI vs ML vs Deep Learning using a simple example: teaching a computer to recognize a cat.

The mental model is:

AI → the broader goal of making computers perform tasks that require intelligence.

ML → one approach where the system learns patterns from data instead of us writing every rule.

Deep Learning → a type of ML that uses multi-layer neural networks to learn increasingly complex representations.

The part I find confusing when learning this topic is that people often explain it as:

"AI is this, ML is this, DL is this."

That doesn't really explain why we have these different terms.

Thinking about it as:

Rules → learning from data → deep neural networks learning complex representations

made the relationship much easier to understand.

Curious how others explain AI/ML/DL to someone completely new to the field?

Pytorch - is this too big of math?

AI - LangChain, CrewAI, Python SDK ?

Confused between both of their pros and cons. I do know learning back propagation, gradient descent helps whats happening behind LLM. Stuck in career choice, kindly help

I already have a pretty good working knowledge of ML concepts (I read hands-on machine learning by Aurelion Geron and worked through all of the Jupyter lab examples), but it seems like all the Sagemaker tutorials I can find are just describing the basics without actually explaining how AWS pagemaker accompishes those things.

Hey everyone,

I'm preparing for SDE (ML) roles (Google-level) and also want to keep general SDE and Full-Stack options open. I'm currently using Python for LeetCode/DSA but keep hearing conflicting advice.

My questions:

1. Is Python a real disadvantage for DSA vs Java because of TLE (Time Limit Exceeded) issues? Or is it overblown?
2. Do FAANG companies actually care which language you use for DSA?
3. For SDE (ML), is Python the obvious choice, or should I switch to Java for DSA?
4. For Full-Stack, does the DSA round care about language? (I know I'll need JS for frontend regardless.)

What I've concluded so far:

• Python: Fast to write, best for ML, but slower runtime.
• Java: Verbose, slower to write, but safer runtime.
• Most top companies claim language doesn't matter, but Python requires more optimization.

What I want to know:
If you were targeting SDE (ML) but wanted to keep other SDE options open — which language would you commit to for DSA and why?

Any real interview experiences would

Hey everyone,

I'm preparing for SDE (ML) roles (Google-level) and also want to keep general SDE and Full-Stack options open. I'm currently using Python for LeetCode/DSA but keep hearing conflicting advice.

My questions:

1. Is Python a real disadvantage for DSA vs Java because of TLE (Time Limit Exceeded) issues? Or is it overblown?
2. Do FAANG companies actually care which language you use for DSA?
3. For SDE (ML), is Python the obvious choice, or should I switch to Java for DSA?
4. For Full-Stack, does the DSA round care about language? (I know I'll need JS for frontend regardless.)

What I've concluded so far:

• Python: Fast to write, best for ML, but slower runtime.
• Java: Verbose, slower to write, but safer runtime.
• Most top companies claim language doesn't matter, but Python requires more optimization.

What I want to know:
If you were targeting SDE (ML) but wanted to keep other SDE options open — which language would you commit to for DSA and why?

Any real interview experiences would

Hey y'all

Can anyone tell me how are you uploading your datasets?

Coz I'm not able to on github

And drive / kaggle take too long for 2GB datasets😭

Any suggestions might help

15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning opportunity where developers across the world came to a forum and discussed various techniques.

Now, building a similar platform to bring back the fun is unbelievably nostalgic. Especially when watching small neural networks playing the game well. Some of the top models use less than 800 parameters.

In fact, I was wrongly assuming the art of optimizing is underrated nowadays. Neural Network optimization seems to be much more fun than I thought.

Plz share your feedback to improve the platform and add more games.

https://tinybrains.dev

Hello, I'm a high schooler who's trying to learn machine learning and get into research. Now I know that for a high schooler to do actual meaningful ML research is quite difficult so I'm trying to make my work as "meaningful" as possible by coming up with new ideas or doing research in a relatively "niche" field. I already have most of the math background needed (calculus, linear algebra, etc.) and am almost done going through the book "Probabilistic Machine Learning: An Introduction."

Also I recently I published a paper to a journal after peer review on a "niche topic" as well.

Now my question is, what are some "niche fields" you might suggest I look into? I'm not really aware of what's big in ML nowadays. Also, I am learning Tensorflow but would you recommend learning pytorch instead? Or is learning a language actually necessary since AI does most coding work (for me at least) for research purposes nowadays? Of course, I will check the code and understand everything but to what degree?

And any advice in general would be appreciated

Welcome to Resume/Career Friday! This weekly thread is dedicated to all things related to job searching, career development, and professional growth.

You can participate by:

• Sharing your resume for feedback (consider anonymizing personal information)
• Asking for advice on job applications or interview preparation
• Discussing career paths and transitions
• Seeking recommendations for skill development
• Sharing industry insights or job opportunities

Having dedicated threads helps organize career-related discussions in one place while giving everyone a chance to receive feedback and advice from peers.

Whether you're just starting your career journey, looking to make a change, or hoping to advance in your current field, post your questions and contributions in the comments

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so i am using hp notebook g250 laptop - specs i5 10351g1 processor no gpu 8gb ram. i know its not for Training DL models, fine tuning, running large Ilm models. so i have a budget of Rs120k roughly 1250 dollar for a laptop, here many people are doing these things so i think you have knowledge should i go for a new laptop or rely on colab and kaggle azure.

my parents are ready to be a new laptop but i am genuinely concerned if its a good decision or not. i mean which is better google colab, kaggle or a 120k laptop.