ML Engineer Jobs:
What Nobody Tells You
36% of ML Engineer postings prefer a PhD. Only 3% are entry-level.
Here's how to compete, or find a smarter path into AI.
You're Competing Against PhDs Who Spent 5 Years Publishing Research.
Is This Fight You Can Win?
You've completed deep learning courses, but interviews want you to derive backprop on a whiteboard.
Job postings ask for 'PhD preferred' and 36% of them mean it. That number is going up, not down.
You could spend 2 years self-teaching and still apply to 200 jobs with zero offers.
Beat the Odds With the Right Strategy.
The World-Class AI Engineer Cohort
Is it impossible to self-teach ML Engineering? No. Neither of PyTorch's co-creators have a PhD. But for every self-taught ML Engineer who made it, hundreds didn't. You need a strategic approach: the right projects, the right positioning, and someone who's navigated this path before.
Assess Your Path
Honest evaluation: ML Engineer or alternative AI roles?
Build Proof
Projects that demonstrate deep understanding, not just syntax
Position to Win
Stand out against PhD candidates through practical skills
Meet Your Mentor
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
Every Month You Wait, the Competition Gets Stronger
Frequently Asked Questions
Should I pursue ML Engineer or MLOps roles?
If you're a software engineer or self-taught developer, MLOps is the more realistic path. You can enter through DevOps first, then add ML knowledge. For ML Engineering, you're competing against candidates who spent 5 years in academia, who can derive algorithms from scratch, who have recommendation letters from professors that hiring managers recognize. MLOps lets you compete on more level ground where projects matter more than degrees.
How important is a PhD for ML Engineer jobs?
36% of ML Engineer job postings list PhD as preferred, and that number is going up, not down. Only 3% of postings are entry-level. When you apply, you're competing against people who spent years publishing research papers and can discuss cutting-edge architectures from first principles. It's not impossible without a PhD, but you need exceptional projects and a strategic approach to stand out.
What do ML Engineers earn?
Entry-level ML Engineers earn $93K-$113K. Senior roles hit $150K-$200K+. At elite AI companies, total compensation can reach $500K at Meta or $800K at OpenAI. But here's what matters more than the salary ceiling: what are your actual odds of reaching it? If you self-teach and spend two years learning, you might apply for 200 jobs and get zero offers. The path matters as much as the destination.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
I've signed up for cohorts before and dropped out. How is this different?
It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.
I'm not pivoting careers. I want to build a product. Does this still work?
Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.
Do I need prior AI experience?
You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.
What does it cost?
It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.
Can I do this while working full-time?
Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.