Embedded Systems Engineer
to AI Engineer
Your low-level expertise is your unfair advantage.
Edge AI and TinyML need engineers who understand hardware.
Feeling Stuck in a Shrinking Niche?
Python and ML frameworks feel foreign after years of C/C++ and register manipulation.
ML theory seems overwhelming when you're used to deterministic, debuggable systems.
Leaving hardware expertise behind feels like abandoning your competitive advantage.
Your Hardware Background Is an Asset, Not a Liability.
The World-Class AI Engineer Cohort
Edge AI is the fastest-growing segment of AI deployment. Companies desperately need engineers who understand memory constraints, power optimization, and real-time systems. Your embedded background positions you perfectly for TinyML, on-device inference, and AI hardware optimization.
Map Your Transferable Skills
C/C++, optimization, memory management, RTOS
Target Edge AI & TinyML
Your niche where hardware knowledge matters
Build ML Fundamentals Strategically
Focus on deployment, not research
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.
Edge AI Roles Are Growing 40% YoY in 2026
Frequently Asked Questions
What embedded skills transfer to AI engineering?
More than you think. C/C++ proficiency transfers directly to model optimization and CUDA programming. Memory management expertise is crucial for deploying models on constrained devices. Your understanding of hardware-software interaction is invaluable for edge deployment. Real-time systems experience applies to latency-critical AI applications. Debugging skills for non-deterministic behavior translate well to ML troubleshooting. Companies building AI chips, edge devices, and embedded ML solutions specifically seek engineers with your background.
How hard is it to learn Python and ML coming from C/C++?
Python is surprisingly easy for embedded engineers. The syntax is simpler, and you'll appreciate the abstraction after years of manual memory management. The real challenge isn't Python; it's shifting from deterministic to probabilistic thinking. ML models don't have clear debugging traces like embedded code. However, your optimization mindset helps with model efficiency, and frameworks like TensorFlow Lite and PyTorch Mobile bridge your hardware knowledge with ML. Most embedded engineers achieve Python proficiency in 4-6 weeks.
What is edge AI and why does it suit embedded engineers?
Edge AI runs ML models on devices rather than in the cloud. Think smartphones, IoT sensors, autonomous vehicles, medical devices, and industrial equipment. This requires model compression, quantization, and deployment on resource-constrained hardware. Your embedded background makes you uniquely qualified because you understand the constraints: limited memory, power budgets, real-time requirements. TinyML specifically targets microcontrollers you already know. Companies like Qualcomm, NVIDIA, Apple, and hundreds of startups need engineers who can bridge ML and hardware.
How long does the transition to AI engineering take?
For embedded engineers, expect 4-6 months of focused learning and project work. The first 4-6 weeks cover Python and ML fundamentals. Weeks 6-12 focus on deployment frameworks (TensorFlow Lite, ONNX, PyTorch Mobile) and edge-specific skills. Weeks 12-20 involve building portfolio projects that showcase your unique embedded+AI combination. Your existing engineering maturity accelerates the process. Many embedded engineers land edge AI roles within 6 months while employed, especially when targeting companies that value hardware expertise.
Do I have to give up hardware work entirely?
Absolutely not. Edge AI and TinyML roles specifically require hardware understanding. AI chip companies, embedded ML platforms, and autonomous systems teams need engineers who can work across the stack. Roles like ML Systems Engineer, Edge AI Engineer, and Hardware-ML Co-design Engineer exist precisely for people with your background. You can stay close to hardware while working with AI. In fact, pure software ML engineers often struggle with deployment on constrained devices, which is exactly where you excel.
What salary can embedded engineers expect in AI roles?
Edge AI and TinyML roles typically pay $150K-$220K for mid-level positions in 2026, with senior roles reaching $250K+. This often represents a 30-50% increase over traditional embedded roles. Embedded engineers with AI skills are rare, creating strong demand. Companies like Apple, Tesla, Qualcomm, and AI chip startups pay premiums for engineers who understand both domains. Your unique combination of hardware expertise and AI capability commands higher compensation than either specialty alone.
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.
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.
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.