AI Engineering Manager Jobs:
Lead the Teams Building the Future
The hardest AI roles to fill aren't technical. They're leadership.
AI Engineering Managers earn $200K-$350K+ bridging the gap between code and strategy.
You're Great at AI. Managing AI Teams Is Different.
Balancing technical depth with management breadth. You can't code all day AND run a team effectively.
Building AI teams is uniquely hard: ML engineers, data scientists, and software engineers all speak different languages.
Companies want managers who understand AI deeply. But most leadership training ignores the ML-specific challenges.
From AI Expert to AI Leader.
The World-Class AI Engineer Cohort
AI Engineering Management isn't just engineering management with AI on top. The role requires navigating ML-specific challenges: experiment tracking, model uncertainty, and cross-functional alignment with data, product, and research. Whether you're a senior IC stepping into management or a traditional EM moving into AI, the transition requires deliberate skill-building.
Clarify Your Path
IC-to-manager vs manager-to-AI: different gaps to close
Build AI Leadership Skills
Team building, stakeholder management, ML project planning
Position for Leadership
Land $200K-$350K+ AI EM roles at top companies
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.
Companies Are Building AI Teams Faster Than Leaders Emerge
Frequently Asked Questions
Should I stay on the IC track or move into AI management?
It depends on what energizes you. AI Engineering Managers spend 60-70% of their time on people (1:1s, hiring, performance), 20-30% on strategy and alignment, and only 10-20% on technical work. If you love deep technical problems, consider Staff/Principal IC paths. If you want to multiply impact through others and shape team direction, management is the right move. Both paths can reach $300K+ at top companies.
What do AI Engineering Managers earn?
AI Engineering Manager compensation typically ranges from $200K-$350K+ total comp, depending on company tier and location. At FAANG and well-funded AI companies, senior AI EMs (Director+) can exceed $400K-$500K with equity. Base salaries are typically $180K-$280K with bonuses and equity on top. The premium over traditional EM roles reflects the scarcity of leaders who combine deep AI understanding with proven management skills.
How do I transition from Senior AI Engineer to AI Engineering Manager?
The IC-to-manager transition requires intentional skill-building before you get the title. Start by taking on tech lead responsibilities: mentoring juniors, leading projects, representing your team in cross-functional meetings. Volunteer to run hiring loops and onboarding. Document your impact in terms of team outcomes, not personal contributions. When you interview, demonstrate that you've already been doing the job unofficially. Many companies prefer promoting internally, so signal your interest early.
Can I transition from traditional engineering management to AI management?
Yes, but you need to close the technical credibility gap. AI teams respect managers who understand what they're building, even if you're not coding daily. Invest time in understanding ML fundamentals, experiment tracking, model evaluation, and production ML challenges. Take an AI project from prototype to production, even as a side project. The management skills transfer directly; the AI-specific context is what you need to add.
What does an AI Engineering Manager actually do day-to-day?
AI EMs split time across several areas: (1) People management: 1:1s, career development, performance reviews, hiring (40-50%), (2) Project/program management: sprint planning, roadmap alignment, dependency management (20-30%), (3) Technical guidance: architecture reviews, technical decisions, unblocking the team (15-25%), (4) Stakeholder management: aligning with product, data science, and leadership (15-20%). Unlike IC work, success is measured by team output, not personal contributions.
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.