AI Platform Engineer Jobs:
Build the Systems ML Teams Depend On
AI Platform Engineers build the internal tools and infrastructure that make ML teams productive.
Salaries range $150K-$220K+ at companies scaling AI.
Platform Engineering for AI
Is a Different Skillset.
The job title is everywhere but nobody agrees what it means. Platform vs MLOps vs Infrastructure vs DevOps for ML. The boundaries blur.
You need breadth across Kubernetes, internal tooling, developer experience, AND ML systems. Most engineers specialize too narrowly.
Building for 5 data scientists is different than 50. You need to design platforms that scale with organizational growth, not just traffic.
Platform Engineering Meets ML Infrastructure.
The World-Class AI Engineer Cohort
AI Platform Engineers sit at the intersection of platform engineering, developer experience, and ML systems. You build the internal platforms that make ML teams self-sufficient: model registries, feature stores, training pipelines, and deployment automation. Companies like Netflix, Spotify, and Uber have entire teams dedicated to this.
Master Platform Foundations
Kubernetes, internal developer platforms, self-service infrastructure
Add ML-Specific Systems
Feature stores, model registries, training orchestration, inference platforms
Position as Platform Leader
Target companies actively building ML platform teams
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.
ML Teams Are Growing Faster Than Platform Support
Frequently Asked Questions
What's the difference between AI Platform Engineer and MLOps Engineer?
MLOps Engineers focus on the ML lifecycle: training, deploying, and monitoring specific models. AI Platform Engineers build the platforms and internal tools that MLOps Engineers use. Think of it this way: MLOps deploys a model to production. Platform Engineering builds the system that makes that deployment possible. Platform roles are more focused on developer experience, self-service tooling, and organizational scale. MLOps is closer to the models themselves.
What skills do AI Platform Engineers need?
The core stack includes: (1) Kubernetes and container orchestration at scale, (2) Internal developer platform tools (Backstage, custom CLIs, self-service portals), (3) ML infrastructure components (Ray, KubeFlow, MLflow, feature stores like Feast or Tecton), (4) Infrastructure as Code (Terraform, Pulumi), (5) Observability and monitoring for ML-specific metrics. The differentiator is building for internal users, not external customers. Developer experience design matters as much as raw infrastructure skills.
What do AI Platform Engineers earn in 2026?
AI Platform Engineer salaries typically range from $150K-$180K for mid-level roles to $180K-$220K+ for senior positions. Staff-level platform engineers at major tech companies can exceed $300K total compensation. These roles command premiums because they require rare combinations of platform engineering, ML systems knowledge, and developer experience expertise. Companies building serious ML capabilities often pay above market to attract this talent.
Which companies hire AI Platform Engineers?
Look for companies with significant ML teams (50+ data scientists/ML engineers) who need dedicated platform support. Top employers include: (1) Tech giants: Netflix, Spotify, Uber, LinkedIn, Meta, Google, (2) AI-native companies: OpenAI, Anthropic, Scale AI, Databricks, (3) Fintech/trading: Two Sigma, Citadel, Stripe, (4) Enterprise tech: Salesforce, Adobe, Snowflake. Smaller companies often combine this role with MLOps. Look for job titles like 'ML Platform Engineer', 'AI Infrastructure Engineer', or 'Machine Learning Platform' in listings.
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.