DeepSeek:

🔹 Sparse Mixture of Experts
🔹 Multi-head Latent Attention
🔹 Model Distillation

What does it really mean for the future of AI?

I’m joining Rotational this Friday, Feb 7 at 12PM ET to break it all down. Join us live on Zoom!

🔗

Pushback on my latest video—some claim seniority is all that matters in ML hiring. That’s just wrong.

What actually gets you interviews?

➜ Pedigree
➜ Referrals
➜ What you can do

These are the three biggest hiring signals in industry.

Stop listening to people who have never hired anyone.

Neural networks claim to mimic biological learning, but their training is nothing like how humans learn.

Imagine if, at birth, you were dumped with 80% of all human knowledge—completely out of order—and never allowed to learn again.

That’s AI today. Maybe we need a new approach?

The Demo Culture is Ruining ML... But It Doesn't Have To Machine learning demos should drive real AI progress, not just hype—learn how to create iterative, production-ready ML systems that truly work.

Too many ML teams use demos to impress rather than progress. A strong demo culture reduces uncertainty, builds trust, and moves toward production-ready AI. Here’s how to fix the broken “demo culture” and use it for real progress:
🔗 https://buff.ly/40Z0HPX

I appreciate what you are trying to do. I have spent 14 years in ML, so may I introduce some nuance?

1. Downloading the model and using it locally does not appear to be sending data back to China. (I haven’t tried it yet)
2. I don’t know of any similar free service where you own your data.

An M2 candidate I’ve been coaching just got a FAANG offer… 3 months after their final interview.

Sometimes ghosting isn’t a no—it’s just an extremely slow hiring process.

Kevin Van Horn - Following Jaynes and Why Fundamentals Matter MLE Path · Episode

Kevin Van Horn (30+ years in ML, ex-Adobe Sr. Staff) joins the podcast to break down Bayesian probability, optimization, and what most ML engineers get wrong. Insightful, technical, and real—tune in now! 🎧

My friend had some great advice: bsky.app/profile/mlep...

Most ML decisions aren’t final—but early in your career, they feel that way.

The best engineers don’t chase perfect solutions; they design systems that are reversible. Learning to distinguish one-way vs. two-way doors is the key to moving fast in ML.

#MachineLearning #TechCareers #AIEngineering

Meta warns that it will fire leakers in leaked memo Mark Zuckerberg told employees that leaking “sucks.”

#Meta warns that is will fire leakers in a leaked memo

Now that's meta.

Had a great chat with a Senior Staff ML candidate in office hours yesterday—it reinforced something important about ML System Design interviews at higher levels.

You need to save time for a deep dive, not just cover all stages.

Here’s how I structure my time to maximize impact.

#TechInterviews

Having assisted numerous candidates in clearing Meta’s ML System Design interviews, I’ve observed that questions typically revolve around:
➜ Recommender Systems
➜ Harmful Content Detection
➜ Topic Modeling (NLP)

To book your personalized mock interview, visit mockdesignround.com #TechInterviews

MLE Path Podcast · Ilya Reznik · 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…

Dr. Rebecca Bilbro’s open-source advice?

➜ Use OSS libraries
➜ Find issues
➜ Fix them with PRs

That small bit of code you always rewrite? Others do too—contribute it upstream.

More wisdom from Rebecca on this week’s podcast: https://buff.ly/40ulLMw

#OpenSource #MachineLearning

My first big launch at Adobe? I was ready. Pushed the code, ran tests, went home.
Next morning: production was down. My change broke everything. I was horrified.
But I took responsibility, fixed it, and earned more trust because of it. Failure isn’t the end—it’s part of the job.
#Engineering #Tech

Big tech hiring is brutal—you need to pass #LeetCode, but spamming 5000 problems won’t get you there.

➜ Learn what companies actually test
➜ Focus on the right prep, not random grinding
➜ Part 1 of 4: Breaking down MLE coding interviews
Read here: https://buff.ly/4jyu7LZ
#DSA #TechCareers

NeurIPS 2024 felt different—less academic, more implementation.

LLMs alone aren’t enough. Reinforcement learning, better evaluation methods, and more complex architectures are the real future.

We’re finally moving past ‘just scale it up.’

#NeurIPS #AI

LLMs are plateauing. OpenAI is struggling for breakthroughs. The ‘just throw more data at it’ mentality is nearing its end.

This is great news for ML. We need diverse techniques, not just one overhyped approach.

#AI #MachineLearning

Using AI for interview prep? Do it right:

➜ Use it to refresh, not to learn from scratch
➜ Ask specific questions with context
➜ Always verify you understand why the answer is correct

AI should enhance, not replace, your learning.

#TechInterviews #MachineLearning

ChatGPT’s bullet-point spacing is weirdly distinct. Once you notice it, spotting AI slop gets a lot easier.

Are we all paying more attention to this, or just me?

#AI #LLMs

Want to be an ML engineer?

➜ Build an ML project for yourself first—make sure you enjoy it
➜ Learn & fill in gaps with courses (you now know what’s missing)
➜ Build something for others
➜ Apply & prep for interviews

Half of this is actively building. That’s how you get ahead.

#MachineLearning

MLE job hunt tip: Batch applications.

➡ Apply to roles with similar requirements at once
➡ Prep for specific interview types
➡ Expand filters only if needed

Avoid burnout and stay focused.

#MLEngineer #JobSearch

Once you hit Senior MLE, what’s next?

➡ Architect: Big-picture systems
➡ Tech Lead: People leadership
➡ Problem Solver: Deep technical challenges
➡ Right Hand: Flexible across roles
➡ Manager: Strategy & teams

The real question isn’t promotion—it’s leadership.

#MachineLearning

OpenAI "needs" trillions of dollars...

My student just crushed the ML System Design interview and landed a Meta E5 offer:

Base: $230k
Bonus: 15%
Sign-on: $80k
RSUs: $875k over 4 years (vesting quarterly with no cliff)

Proof that hard work and preparation pay off. Huge congrats!

#MachineLearning #TechInterviews

At 19, my first ML project came from wanting to find a picture of my dad. Too many photos, no ML experience—but the project drove my learning.

You don’t need to wait. Start building. Solve a problem that matters to you and let it guide your growth.

#MachineLearning

📊 Conducted 100+ ML System Design interviews at big tech—here’s what I’ve learned:

➜ Mismanaging time = running out
➜ Skipping stages = missing the big picture

Stay clear, follow these stages, and nail your interview. Timing is everything ⏳

#MLSystemDesign #AI #TechInterviews #BigTech

At Twitter, I shadowed an ML interview. The candidate chose C++—they were strong, but boilerplate cost them time and impact.

ML engineers: use Python. It’s concise, expressive, and the industry standard. Don’t let your language choice hold you back.

#Python #MachineLearning

AGI hype scares me—not because it’s imminent, but because we may have "Boy who cried wolf" problem. My take: https://buff.ly/4ha9Yu9
#AI

Deepseek R1 is proof that censoring LLMs is harder than anyone expected. The secrets it reveals? You don’t want to miss this. Watch the video: [link]

#AI #LLMs #TechInsights

🚀 Announcing MLE Path Podcast!

🎙️ Candid conversations with senior ML engineers and hosted by an industry veteran (Adobe, Twitter, Meta).

🎧 Available now on Spotify, Apple Podcasts, and wherever you listen to podcasts!
#MLEPath #MachineLearning #AI #TechPodcast