AI Testing Engineer Program
Build the skills that matter. Our 16-week intensive program prepares you for real-world AI system testing through hands-on projects and mentorship from engineers who've been in the trenches.
Four Phases That Build on Each Other
We don't believe in cramming random topics together. Each phase prepares you for the next, starting from fundamentals and moving toward specialized testing scenarios you'll encounter in actual AI deployments.
Foundations & Testing Basics
Understanding how AI systems work before you can test them properly. We cover neural network architectures, data pipelines, and the common failure points that every tester needs to recognize early.
Automated Testing Frameworks
Building test suites that scale. You'll work with pytest, Docker containers, and CI/CD pipelines to create automated tests that catch issues before models hit production environments.
Bias Detection & Safety Testing
The tricky part that separates junior from mid-level testers. Learn to identify algorithmic bias, test for edge cases in NLP models, and document potential safety concerns in deployment scenarios.
Production Monitoring & Capstone
Real-world project time. Work with a team to test an actual AI system from requirements through deployment, including monitoring dashboards and incident response procedures.
Learn From People Who've Done This
Our instructors aren't career teachers. They're working engineers and QA leads who test AI systems during the day and mentor students in the evening because they remember what it was like to break into this field.
Vernon Chen
Lead QA Engineer
Spent eight years at healthcare AI companies finding edge cases in diagnostic models. Known for explaining complex testing concepts using everyday analogies.
Marcus Riley
Testing Infrastructure Specialist
Built automated testing pipelines for computer vision systems at three different startups. Believes that good tests should fail loudly when something's actually wrong.
Dwayne Kowalski
Safety & Ethics Consultant
Specializes in bias detection and fairness testing for NLP systems. Previously worked with financial services companies on algorithmic accountability.
How We Actually Teach This Stuff
Most bootcamps throw frameworks at you and hope something sticks. We take a different route: small cohorts, lots of pair programming, and weekly code reviews where you defend your testing decisions to engineers who've seen it all go wrong in production.
Project-First Learning
You're building test suites from week two. Theory comes when you need it to solve actual problems, not before.
Real Data, Real Problems
Work with messy datasets and flawed models that mirror what you'll encounter in actual AI deployments.
Weekly Technical Interviews
Practice explaining your testing approach to non-technical stakeholders and defending technical choices to senior engineers.
Open Source Contributions
By week 12, you'll contribute test coverage to actual open source AI projects, building your public portfolio.
