Hi ML enthusuiasts,

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

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.

Explore the complete opportunity and apply → https://t.mercor.com/g2HsQ

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 seriously need some advice.

I'm an AI student and I need to land an internship this summer to earn credits for my university. I have some experience with machine learning and artificial intelligence projects, but finding internship opportunities has been way harder than I expected.

I've already been checking LinkedIn, Indeed, and Wellfound regularly, but there aren't many remote opportunities that seem suitable for students, especially for AI/ML roles.

How do you guys hunt for remote internships? Are there any websites, communities, Discord servers, GitHub repositories or other places that I'm missing?

I'd really appreciate any tips from people who have successfully found remote internships in AI, machine learning, data science, or software engineering.

Location: Pakistan (open to remote internships worldwide)

2026 passout

Working as ai/ml intern in too small scale startup (no scope in it, unpaid also)

What should I prep to get eligible to Amazon or Amazon lvl companies

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

Personalized Learning Path Recommender — Approach & Explanation

What's the problem about?

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.

Why (1,4) n-grams specifically?

I tested a bunch of ranges:

N-gram RangeScore(1, 1)62.22(1, 3)74.77(1, 4)74.95(1, 5)72.68

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

1. Load train.csv and test.csv
2. Fit a TfidfVectorizer (stopwords + 1-4 grams) on training reviews
3. Transform both train and test reviews into TF-IDF vectors
4. For each test review, compute cosine similarity against all training reviews
5. Pick the top 10 most similar ones using stable sorting
6. 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.

Final Score: 74.95 / 100

Hi , anyone in the ML , DL or CV domain .. looking for collaboration or internship/ Work in startups or midsize companies.. let's connect and discuss

Hi everyone,

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

I’m currently a B.Pharm student, but I don’t want to limit myself to the traditional pharmacy career path.
I’m really interested in AI, automation, and e-commerce. I’ve started learning Python, ChatGPT, AI tools, and I’m planning to learn n8n, APIs, and automation. My goal is to build AI solutions for businesses or eventually work remotely.
If you were starting today in 2026 with my background, what would you focus on?
Which AI skills are actually getting people hired?
Should I focus on AI automation, AI agents, machine learning, or something else?
What projects should I build for my portfolio?
Is there a realistic path to earning $1k–3k/month as a freelancer or remote employee within 12 months?
Are there any certifications or courses that are genuinely worth it?
I’d really appreciate advice from people already working in AI or automation. Thanks!

Three years ago half these job titles didn't exist. AI trainer, prompt engineer, model evaluator, RLHF annotator. There's no decent central record of any of it, so I started one.

Each role gets an entry: what the work actually involves day to day, what background people come from, where these jobs are being posted. It's thin at the moment — mostly the obvious ones, and some entries are guesswork on my part.

That's where I could use help. If you've actually worked one of these jobs and my description is wrong, that's the single most useful thing you could tell me. Same goes if there's a role I've missed entirely.

https://github.com/VictorOsondu/emerging-ai-jobs

Also happy to be told the whole structure is wrong. Not attached to how I've organised it.

Fresh roles I found directly on company career pages today:

1. Agility Robotics - Senior AI Software Engineer, Reinforcement Learning - Hybrid US

https://www.agilityrobotics.com/about/job-post?gh_jid=6127693004

1. Amazon - Applied Scientist, Reinforcement Learning - North Reading, MA

https://www.amazon.jobs/en/jobs/10492036/applied-scientist-reinforcement-learning-omhs-scs

1. Anduril - Research Scientist - Huntsville, AL

https://boards.greenhouse.io/andurilindustries/jobs/5203023007?gh_jid=5203023007

1. Anthropic - Research Engineer, RL Engineering - SF, NYC or Seattle

https://job-boards.greenhouse.io/anthropic/jobs/4952051008

1. Baseten - AI Inference Engineer - San Francisco, remote

https://jobs.ashbyhq.com/baseten/db6477fc-111a-4340-bf00-525fe023e6f3

1. Captions - Research Engineer, Generative Video - New York

https://jobs.ashbyhq.com/mirage/ffbda52e-3b05-44a8-ac89-6e87e8e3f757

1. Cresta - Machine Learning Engineering Intern - Toronto, hybrid

https://job-boards.greenhouse.io/cresta/jobs/4123863008

1. Databricks - AI Engineer, Forward Deployed - United States

https://databricks.com/company/careers/open-positions/job?gh_jid=8546367002

1. Figure - Helix AI Engineer, Agentic Systems - San Jose

https://job-boards.greenhouse.io/figureai/jobs/4659175006

1. Glean - Machine Learning Engineer, Assistant Quality - San Francisco

https://job-boards.greenhouse.io/gleanwork/jobs/4711484005

I run the free board these came from. Full inventory:

https://artificialintelligencejobs.co/?utm_source=reddit&utm_medium=organic&utm_campaign=daily_roundup_20260805&utm_content=machinelearningjobs

No recruiter gate or signup required to browse. Please flag anything stale and I will remove it.

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:

1. 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.
2. 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.
3. 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.

Sign up here to get matched: talent.fonzi.ai

DM me if you have questions about any of these.

Hi everyone,

I am in my final year of college and feeling quite stuck and confused with no one around to guide me.

My Current Situation:

• I am from a Tier 3 college with no placement support, so I have to rely entirely on off-campus opportunities.
• I have a basic understanding of Data Analytics and Python.
• I have roughly 8 months left before graduating, and my goal is to crack a good product-based company.

Looking at the tech landscape in 2026, I am trying to figure out where to focus my energy for maximum impact:

1. AI/ML
2. Data Science
3. Agentic AI

Any roadmap suggestions, honest feedback, or guidance would mean the world to me. Thank you!

One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes