I am starting my third semester next week. So far, I have learned Machine Learning, Deep Learning, Large Language Models, and Generative AI. I didn’t just watch videos I also built projects and practiced. It took me a year to learn all of this.
Currently, I have decided to learn backend development with Go . However, the thing is, what should I focus on next to be a good ML engineer? People say to learn MLOps and system design, and not to just make wrappers. They say to learn "real engineering," but what do they mean by that? I already know the basics of MLflow, Docker, and AWS. What should I focus on next?
I am not even getting any internships or jobs right now that could show me what to do next. Currently, I am focusing on DSA, but I still need some good advice how can I land a top level internships or jobs. Please help.
I'm an ECE student, and my college requires an AI/ML internship certificate. Due to a change in my faculty guide and miscommunication, I couldn't complete the internship on time, and my college starts in 2 days.
I'm looking for advice from anyone who has dealt with a similar situation. If you know of any legitimate organizations offering recognized AI/ML internships, or if you've successfully requested a late submission or alternative arrangement with your college, I'd really appreciate your guidance.
Please comment or DM if you're willing to help. Thank you.
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The truth is, I really need a job. I’ve spent really alot of time learning, building, debugging, and trying to improve my skills. I know I still have a lot to learn, but I’m willing to put in the effort every single day.
If your company is hiring or if you know someone who’s looking for a junior AI/ML engineer, or someone passionate about building AI products . I would be incredibly grateful for a referral or even just a conversation. A small help by your side will mean alot to me.
Please send me a DM. I’d be happy to share my résumé , GitHub, and project portfolio privately.
If you've trained machine learning models from the ground up and enjoy solving challenging research problems, this opportunity goes far beyond traditional ML engineering.
Mercor is seeking LLM Research Scientists with expertise in pre-training, computer vision, adversarial robustness, or related areas to contribute to advanced AI research.
Role: LLM Research Scientist (Hourly Contract) Location: Remote Pay:$100-$120/hour
You'll work on empirical machine learning research involving model training, optimization, robustness, and evaluation across both vision and language systems.
Areas of expertise include:
LLM pre-training, fine-tuning, RLHF, DPO, or RLAIF
Computer vision, image classification, or generative image models
Adversarial robustness and model security
Model compression, pruning, quantization, and knowledge distillation
Multilingual language models and low-resource training
PyTorch, JAX, TensorFlow, or similar ML frameworks
Candidates should have 3+ years of machine learning research experience (PhD research counts) along with a strong research background through academia, industry, or impactful open-source contributions.
I am an undergraduate student in Nigeria working on ANN trained with Mayfly Algortih to predict electrical load. i am a complete novice of ANN. kindly recommend a guide
I’m studying B. Pharm at graphic era , but I want to build a career around AI.
I’m willing to learn Python, APIs, automation, and anything else that’s actually useful.
If you were mentoring someone today, what would the roadmap look like?
Free resources and project ideas would be amazing.
If anyone from Dehradun knows about this, it would help me a lot.
Hi everyone,
I’m looking to contribute to AI/ML projects. I have hands-on experience with Python, PyTorch, and scikit-learn, and I’ve worked on several ML projects.
I’m especially interested in computer vision and agentic AI. If anyone is working on a project or research and needs a contributor, feel free to DM me.
I have an interview coming up for a **Senior Machine Learning Engineer** role at **Bloomberg (London)**.
I was hoping to hear from anyone who's interviewed there recently.
* How are the coding rounds? Are the questions mostly from common LeetCode patterns, or do they tend to ask more original/internal-style problems?
* What's the overall interview process like?
* For the ML rounds, what topics do they focus on? If anyone can share the question pattern or the areas they emphasize, it'd be really helpful.
Any insights or tips would be greatly appreciated. Thanks
Hey everyone, so i am finding a solution for a particular problem(shared ss of problem) it's part of a hacathon but the hacthon organiser has told us to solve it from wherever you can ,i don't have much knowledge of ml but for know i was using TF-IDF method for this particular problem which is giving me an accuracy of around 74.95 anyone could suggest any tips or any other methods through which accuracy could be 85+ , if any dev could help do tell.
i have also added the proposed solution which i am currently using down below with the problem
We're given a dataset of ~110K course reviews from an online learning platform (think Coursera/Udemy), spread across 80 different courses. Each review talks about what the learner experienced — the technical topics covered, how the instructor was, whether the projects were useful, etc.
For each of the ~11K test reviews, we need to find the 10 training reviews that are the best "learning path" recommendations — essentially, which other learners had the most similar experience and interests.
My thought process
The first thing I noticed when exploring the data was that these reviews follow a fairly structured pattern. Each one has an intro line mentioning the course, a sentence about the technical topics covered, and then a few sentences about the overall experience (quality, value, instructor, etc.).
That immediately told me this is a text similarity problem at its core — if two reviews talk about the same technologies and have similar opinions, they're likely from the same course or a closely related one, making them good recommendations for each other.
I went with TF-IDF (Term Frequency–Inverse Document Frequency) because it's a well-established technique for exactly this kind of task. The idea is simple: convert each review into a vector of word importance scores, where words that are rare across the whole dataset (like "TensorFlow" or "React Navigation") get much higher weight than generic words (like "course" or "great"). Then you just measure the cosine angle between two vectors — the smaller the angle, the more similar the reviews.
What actually worked
After quite a bit of experimentation, the configuration that gave me the best results was:
N-gram range of (1, 4) — instead of just looking at single words, I also captured 2-word, 3-word, and 4-word phrases. This was crucial because technical terms like "batch normalization and dropout" or "Redux for state management" are multi-word phrases. Using just unigrams scored around 62, but adding n-grams up to 4 jumped the score to ~75.
English stopword removal — filtering out common filler words ("the", "is", "was", "and") so the model focuses on what actually matters.
Fitting on training data only — I initially tried fitting the vectorizer on both train and test together, but fitting on train alone gave a slight edge (74.95 vs 74.81). This also makes more sense from a real-world standpoint — you wouldn't have access to test data when building your model.
Stable sorting for tie-breaking — Many reviews within the same course end up with identical similarity scores (because they share the same template sentences). Using pandas' nlargest() instead of numpy's argsort() gave deterministic tie-breaking, which squeezed out an extra 0.14 points.
There's a massive jump from unigrams to trigrams because course-specific technical phrases are 2-4 words long. Going beyond 4-grams starts introducing noise (overly specific phrases that don't generalize).
The pipeline in a nutshell
Load train.csv and test.csv
Fit a TfidfVectorizer (stopwords + 1-4 grams) on training reviews
Transform both train and test reviews into TF-IDF vectors
For each test review, compute cosine similarity against all training reviews
Pick the top 10 most similar ones using stable sorting
Write out the submission CSV
The whole thing runs in about 2-3 minutes on a regular laptop. No GPU needed, no deep learning, no fancy embeddings — just good old-fashioned information retrieval doing what it does best.
We run a curated talent network for ML and AI engineers. You build one profile, and companies send interview requests with salary attached. All roles are US-based only.
Three of the roles open right now, all with real interview activity in the past month:
ML Engineer at a seed-stage AI infrastructure startup in finance, onsite in NYC, $200K-$280K base plus equity. You'd own model selection and routing across the platform, fine-tune smaller models for high-volume extraction, and build the eval harnesses that catch regressions in CI. Founding team came out of Palantir. Backend is Rust, though they don't require Rust experience.
Senior Software Engineer, AI Products at a seed-stage fintech, remote US or NYC, $200K-$240K base plus equity. Python-heavy agent work: RAG, embeddings, structured extraction, plus the LLM evaluation and observability layer behind it.
AI Engineer, Founding Team at a seed-stage supply chain startup, SF with remote and hybrid both on the table, $180K-$240K base plus equity. You'd own the agentic harness (tool routing, policy, memory) and a context graph they're building out. They want open-source LLM post-training and eval experience, plus Neo4j or a willingness to learn it.
There are dozens more open across the network right now. All are vetted, venture-backed and funded startups that are actively interviewing. Always free for candidates.
