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DeepSeek is shaking up the AI landscape, but its real impact might be in the engineering behind it.
Let’s talk:
🧠 Sparse Mixture of Experts
🧠 Multi-head Latent Attention
🧠 Model Distillation
Are these the future of AI models? I’m joining the team at Rotational this Friday, Feb 7 at 12PM ET to break it all down.
🚀 Join us live on Zoom!
🔗 https://buff.ly/4b6g7FD
Got pushback on my last video—some claim seniority is all that matters in ML hiring. That’s completely false.
Here’s what actually gets you in the door:
🔹 Pedigree – A Stanford undergrad, a Bengio lab paper, FAANG experience.
🔹 Referrals – The closer the connection, the stronger the signal.
🔹 What you can do – The right frameworks, a project people actually use.
These are the three biggest hiring signals in industry.
#MachineLearning #TechCareers #Hiring
Neural networks are said to be ‘inspired by biology,’ but their learning process couldn’t be more different.
Imagine if, as a newborn, you were given 80% of all human knowledge—shuffled randomly, with no logical progression—and then never taught anything again.
That’s how we train AI today. Maybe it’s time to rethink our approach?
Just heard from an M2 candidate I’ve been working with—after waiting 3 MONTHS, they finally got a FAANG offer.
Not all ghosting means no… but also, no one should have to wait that long for an answer.
Early in your ML career, every decision feels irreversible. But the best engineers don’t aim for perfection—they build with reversibility in mind.
Understanding the difference between one-way and two-way doors will help you iterate faster and build better.
#Meta warns that is will fire leakers in a leaked memo
Now that's meta.
https://buff.ly/4aBqUqV
Had a great conversation with a Senior Staff ML candidate in my office hours yesterday—it reinforced something important:
At higher levels, ML System Design interviews require a deep dive. If you don’t structure your time right, you’ll run out before getting to the details that matter.
Here’s how I recommend structuring your time.
By the way, I host office hours every week, sign up link in Substack email each Monday at 3PM Pacific. First come, first served.
📍 Sign up: https://mlepath.substack.com
Join MLE Path, the podcast that explores what it truly means to grow and thrive as a machine learning engineer. Hosted by an industry veteran with over 14 years of experience—including leadership roles at Adobe, Twitter, and Meta—this podcast brings you. Click to read MLE Path, by MLEpath, a Substack publication.
Preparing for a Meta ML System Design interview? Their questions often focus on just 3 key areas:
➜ Recommender Systems – Ranking & personalization
➜ Harmful Content Detection – Filtering spam & misinformation
➜ Topic Modeling (NLP) – Structuring massive text data
Focusing your prep on these areas will make a huge difference. I help candidates clear this round every month—if you need a mock interview, I’m here: https://mockdesignround.com 🚀
