Flatiron’s new Work-Integrated Immersive programs are designed for students who want a high-bar path into building products. Our Immersive students learn through structured coursework and support while also working with an employer as a paid apprentice.
The result is verified experience in shipping deliverables, collaborating in real workflows, taking feedback, and building the professional confidence that only comes from contributing in production environments as the tools and expectations keep changing. (See: Work-Integrated Learning: The Next Era of Tech Education)
What is the Work-Integrated Program?
Flatiron’s new Work-Integrated Immersive Program blends coursework with a paid apprenticeship at an employer partner, running 14 to 18 months depending on the track. Apprentices spend 20 hours per week in coursework and 20 hours per week working for a company. The result is verified production experience, a professional portfolio, and earnings that can fully offset tuition.
What Flatiron’s model is optimized for…
- Real work: shipping production-facing contributions with employer partners
- Rigor: clear standards, transparent expectations, and consistent feedback loops
- Selectivity: a serious path with earned participation and earned opportunity
- Dual skill: develop a cross-discipline skill sets (e.g. software engineering and AI)
- Mentorship: support that raises the quality of the work
How it differs from other paths…
- Certificate Programs: You can quickly learn a technical foundation and create portfolio projects that prepare you for jobs. In contrast, work-integrated places you in a real workflow where you ship production-facing work while continuing to learn.
- Internships: May provide exposure to an industry but not always ownership. Work-integrated learning centers on ownership, execution, and accountability.
- Self-study: You can move at your own pace, but there is no structured feedback or exposure to collaboration. Work-integrated learning provides shared responsibility within team environments, along with guidance through technical and professional mentorship.
One Work-Integrated Immersive Program: Two Paths
Pathway A: Accelerated AI Engineering Immersive
The Accelerated Immersive is built for experienced, practicing software engineers (people who already write production code and want to move into AI and machine learning). They start the apprenticeship in week one, running coursework and paid work in parallel from day one.
- Who it fits: practicing software engineers with production coding experience (frontend, backend, or full-stack) looking to transition into AI/ML or AI-integrated engineering roles
- What to expect: AI and data depth, plus immediate production exposure through a paid apprenticeship starting Day 1
- Time commitment: 20 hrs/week of coursework + 20 hrs/week of apprenticeship
- How it works:
- Weeks 1-40: AI & Data Science coursework (15-20 hrs/week) + paid apprenticeship (20 hrs/week)
- Weeks 41-56 (Capstone): AI capstone (10-20 hrs/week coursework) + 30 hrs/week apprenticeship
Pathway B: AI Engineering Immersive
The Immersive path is built for people who are just getting started in tech (early-career starters or professionals pivoting from non-technical fields). These students need to build a strong software engineering foundation before moving into an apprenticeship, so the first four months are a full-time immersive ramp covering full-stack development (JavaScript, React, Python, databases).
- Who it fits: career switchers and early career learners who need a full Software Engineering foundation before moving into AI
- What to expect: a structured, full-time ramp in Phase 1, followed by work-integrated learning in Phase 2 with a paid apprenticeship
- How it works:
- Phase 1 (Months 1-4): Full-time Software Engineering foundations (40 hrs/week), no apprenticeship
- Phase 2 (Months 5-18): AI coursework (20 hrs/week) + paid apprenticeship (20 hrs/week)
The Difference Between Pathways A and B
The core difference is where each group starts based on the skills they bring in. People just getting started in tech need the software engineering foundation first. Whereas, practicing engineers are looking for depth and applied AI production experience from the outset. The curriculum, pacing, and entry points are tailored to meet each group where they are without making either group feel like they are in the wrong room.
Note: We have Cyber Engineering Immersive programs coming soon.
Selection and Readiness: What it Takes to be Successful Here
The Work-Integrated Immersive is selective by design. The competitive interview process ensures students are ready to meet employer expectations.
What we screen for:
- Professional reliability: clear communication, consistent follow-through, and the ability to manage commitments
- Evidence of building: projects, work artifacts, and problem solving/technical ability for those applying for the Accelerated AI Engineering Immersive
- Coachability and judgment: the ability to take feedback well, adapt quickly, and make thoughtful decisions
What Engineers Get: Real Experience, Immediate Contribution, Mentorship, and Durable Leverage
Engineers leave with more than a credential. They leave with competence, confidence, and momentum.
- Paid experience and a credible body of work you can point to
- Mentorship and coaching that raises quality without lowering standards
- A structured learning plan tied directly to deliverables
- Feedback loops that build the capacity to operate inside volatility
- Learned adaptability to keep retooling and compounding skills as the market shifts
What Employer Partners Get: Contributors Who Can Support Real Work
Employer partners get a talent growth system with selectivity, structured mentorship, and tight feedback loops built-in. The result is a predictable pipeline of engineers who don’t just know the tools, they know how to keep learning as the tools change. Since capability is built on the job, partners can strengthen retention and internal progression with confidence in quality.
What Our AI Engineering Students Say
The best way to understand what this model feels like is to hear it from the students doing the work. These voices capture what is hard to convey in a program description: What is it like to ship alongside a team, get mentored in real time, and build confidence by contributing to real deliverables?
“I’ve gained valuable experience and new skills, including contributing to production codebases, collaborating with teams to complete user stories, participating in code reviews, and analyzing, cleaning, visualizing, and discovering patterns in large datasets.
What I’ve enjoyed most so far is the real-world application of what I’m learning, along with the mentorship and collaborative team environment.”
— Nicholas Driver, Accelerated AI Engineering student
“It provides a strong foundation to deepen my understanding of artificial intelligence and data science while working under the guidance of an experienced mentor. I see this experience as an important step in opening new doors and accelerating my professional growth.”
— Dylan Antelme, Accelerated AI Engineering student
“This is a big step for me because I get to learn and work at the same time, applying my development experience while expanding into AI and data-driven technologies. So far, I’ve been working with real tools, real projects, and an amazing community that makes the learning process practical and collaborative. That has been my favorite part.”
— Aiman Lahmamsi, Accelerated AI Engineering student
“I came in with some Python knowledge, but diving deeper into python and data science has been valuable and I’ve loved being able to contribute to existing codebases and collaborate on real projects — not just exercises, but actual work. The Flatiron mentors have been incredibly supportive, and my fellow engineers are amazing and I have learned so much from them. There’s something powerful about growing together.”
— Carolyn Whelpley, Accelerated AI Engineering student
“What I’ve appreciated most so far include structured mentorship, real-world datasets, collaborative engineering environments, and learning to think in models, not just features.”
— Istafa Marshall, Accelerated AI Engineering student
Ready to Build Leverage in a World of Accelerating Systems?
Explore our programs and see which path fits your starting point, goals, and schedule.
If you want a closer look at what it takes to earn a paid apprenticeship and how selection works, visit our Work-Integrated page for a clear breakdown of the interview process.
