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Pathway: Applied AI Engineering

Cost: No-cost
Timeline: 10 weeks
Commitment: 4 hours per week

The Applied AI Engineering pathway equips you to code, build, and collaborate in the AI-powered environments shaping today's tech industry. You'll learn computer science fundamentals and modern software engineering while working directly in AI-assisted workflows, practicing how to debug, test, and refine code with AI as a strategic partner, always with human judgment in control. In the first course, you’ll strengthen your core programming skills and learn to think critically about AI-generated code. Future courses will build on this foundation, guiding you from AI-augmented development toward designing full applications, integrating advanced AI techniques, and contributing to open-source projects. Across the sequence, you'll develop the technical depth, collaboration habits, and professional fluency to thrive as an AI-native engineer.

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Course Details

 

Program Dates & Times

Program Dates & Times

Meets weekly on Wednesdays,
(6 PM - 8 PM PT / 9 PM - 11 PM ET) from Feb 25 - Apr 29, 2026

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Application Deadline

February 1, 2026, at 11:59 PM PT

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Location

These are virtual classes.

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About the Course

CodePath’s Applied AI Engineering Pathway is a two-course sequence that prepares computer science students to thrive as AI-native engineers. This pathway responds to a fundamental shift in the software industry: today’s top employers expect engineers not only to master core CS fundamentals, but also to work effectively, responsibly, and creatively with AI tools.

The first course, Foundations of AI Engineering (coming this Spring 2026!), builds a strong programming foundation and introduces students to AI-assisted development workflows that enhance coding, debugging, and design.

The second course (coming later in 2026), takes learning to the next level through hands-on, professional practice—including real-world open-source contributions that showcase readiness for AI-driven engineering roles.

Why You Should Take This Course

Most AI courses teach tools. This one makes AI part of how you code.

 

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Work on Real-World, AI-Powered Projects

Build portfolio-ready applications like chatbots and summarization tools that demonstrate your ability to deliver solutions incorporating AI as a core feature.

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Think Like an AI-Native Engineer

Go beyond just using AI tools. Learn to critically evaluate, debug, and improve AI-generated code to develop the judgment that employers truly value.

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Collaborate in Open Source

In the advanced class, you'll get to contribute to real repositories and graduate with verified, public projects that employers can see.

Apply today

What You'll Learn

Launching this Spring 2026:
Foundations of AI Engineering

  • Application of data structures, algorithms, and object-oriented programming in AI-assisted workflows.

  • Critical evaluation of AI-generated outputs, with emphasis on debugging, refining, and improving code.

  • Practice reviewing and explaining AI-generated code to prepare for technical interviews.

  • Small-scale projects where AI is integrated as a product feature (e.g., chatbots, summarization, documentation tools).

  • Introductory exposure to advanced AI techniques such as retrieval-augmented generation (RAG), agentic workflows, lightweight fine-tuning, and guardrails.

  • Introduction to open source collaboration, including GitHub workflows, contribution etiquette, and professional conventions.

Coming Soon:
An Advanced AI-Native Engineering and Open Source Capstone Course

  • Onboarding into large, unfamiliar codebases with AI-assisted navigation and debugging.

  • Application of spec-driven "AI vibe coding" and advanced AI workflows.

  • Collaboration with maintainers and peers via professional Git workflows and review processes.

  • Public, verifiable open source contributions as the capstone artifact for portfolios.

Eligibility

  • You are 18 years old or older by the start of the course.
  • You are or will be in the US for the duration of the course.
  • You are or will be enrolled in a US (Puerto Rico included) college or university, or are a recent graduate, for the duration of the class.
    Graduates from September 2025 onwards are welcome to apply.
  • You are pursuing a degree in Computer Science, Software Engineering, or CS-related or Software-related.
  • You have completed at least one foundational programming course (e.g., Intro to CS or equivalent) or demonstrated proficiency in a language such as Python or Java.
  • You have an understanding of loops, conditionals, arrays, data types, and functions.
  • You can set aside 4-6 hours/week, including class times.
  • Your computer is equipped with a webcam and microphone, which you are willing and able to use in all virtual sessions as part of your active participation.
  • You agree to abide by CodePath's Code of Conduct.

How to Apply

The application is a two-step process:

  1. Fill out a 10-15 minute application about your previous exposure to programming, your interest in a career in tech, and general information about you as a person. NOTE: You will need a GitHub account in order to complete the application.
  2. Complete a HackerRank Assessment. This is a 90-minute timed assessment designed to verify that students possess the programming fundamentals necessary to succeed in the course. 

Applicants must complete the application and HackerRank Assessment to be considered for admission. Most students receive a response within two weeks of submitting their HackerRank Assessment. All admissions decisions will be sent via email.

Our courses are open to students of all backgrounds, fostering a learning environment where emerging engineers can develop the skills, support, and connections needed to secure the most competitive technical roles.

Events

Dec

4

Courses

Spring '26 Courses Info Session

Virtual •
5:00 PM - 6:00 PM PST
Dec

17

Courses

CodePath Spring '26 Courses: "Know Before You Go"

Virtual •
10:00 AM - 11:00 AM PST

SUPPORT

FAQs

  • What is the CodePath Applied AI Engineering course?

    CodePath's Applied AI Engineering pathway is designed to help computer science students master software engineering in an AI-native world. Rather than treating AI as a separate topic, the program embeds it directly into programming practice. Students strengthen their understanding of data structures, algorithms, and object-oriented programming while learning to use AI tools as collaborators for debugging, scaffolding, and refinement, always with human oversight and critical evaluation.

  • Why should I take this CodePath course?

    This no-cost 10-week course from CodePath will prepare you for how software is actually built today, where AI is always in the loop. You'll learn to code, design systems, and debug in environments that mirror professional practice, gaining the skills employers now expect:

      • Apply core CS fundamentals using AI as a collaborative tool.

      • Evaluate, refine, and explain AI-generated code confidently.

      • Gain exposure to industry-standard workflows and tools such as GitHub, Copilot, and modern AI-powered IDEs.

      • Develop portfolio-ready projects that showcase your technical reasoning and AI fluency.

      • Strengthen interview-style communication by practicing how to explain design decisions and AI-assisted solutions.

    You'll learn from industry-vetted instructors, collaborate with peers nationwide, and join a network of emerging engineers supported by major tech partners.


  • What does "AI-assisted programming" mean in practice?

    It means that whenever you write, test, or debug code, you will learn to:

    • Use AI to generate or refactor code from specifications

    • Debug with AI while verifying correctness and intent

    • Critique and improve AI outputs

    • Collaborate with AI as you design, document, and test your work


    The human is always in charge, but AI is always in the loop.

  • How is this different from other AI courses?

    Most AI courses teach AI tools as standalone skills. In this program, AI is integrated into the act of programming itself. You'll learn computer science fundamentals, software engineering practices, and advanced AI concepts in an environment where AI is always present as a collaborator.

  • What topics do these courses cover?

    In the foundations course, you'll strengthen your programming foundations while integrating AI into your workflow. Core topics include:

    • Data structures, algorithms, and object-oriented programming in Python.

    • Critical evaluation and refinement of AI-generated code.

    • Algorithmic thinking and prompt engineering.

    • Machine learning literacy (supervised, unsupervised, generative models).

    • Exploratory practice with Retrieval-Augmented Generation (RAG), agentic workflows, lightweight fine-tuning, and AI guardrails.

    • Professional collaboration and version-control workflows using Git and GitHub.

     

    In the advanced course, you'll apply these foundations in real, open source engineering environments:

    • AI-augmented collaboration in large codebases.

    • Spec-driven "vibe coding" and debugging.

    • Integration of advanced AI techniques (RAG, agents, fine-tuning, guardrails) at production level.

    • Professional Git workflow, code reviews, and contributions merged into public repositories.

    • Portfolio-ready deliverables that demonstrate AI-native engineering practices.

  • Will I learn machine learning in this program?

    You'll gain literacy in high-level ML concepts – enough to understand what models are, the difference between supervised and unsupervised learning, and why these approaches matter. You won't need advanced math or statistics. The goal is to give you the language and perspective to talk about machine learning intelligently, while focusing your hands-on work on AI-assisted programming.

  • Will I get to build projects that include AI as features?

    Yes! In the foundations course, you will design and build projects that integrate AI both as a development partner and as a product feature. Early projects may include chatbots, summarization tools, or automated documentation assistants, and you’ll also gain introductory hands-on exposure to advanced techniques such as retrieval-augmented generation (RAG), agentic workflows, fine-tuning, and guardrails.

    In the advanced course, you'll apply these capabilities at scale in open source projects, producing portfolio-ready contributions that demonstrate your ability to engineer reliable, production-style AI features.

  • How will this help me in interviews?

    Employers increasingly ask candidates to interpret, review, and explain AI-generated code. You'll practice these skills alongside debugging, testing, and explaining your reasoning. Combined with your project work and open source contributions, you'll leave with strong preparation for technical interviews.

  • Will I get exposure to open source?

    Yes! In the foundations course, you’ll be introduced to open source practices such as GitHub workflows, contribution etiquette, and collaboration conventions.

    As you progress to the advanced course, you'll contribute directly to real-world open source repositories, building professional experience and portfolio-ready artifacts.

  • Do I need prior AI experience?

    No. The courses are designed to start with the fundamentals while integrating AI into the process from the beginning. You'll learn to program in an AI-native way without needing prior exposure to AI or ML.

  • Do I need any other previous experience?

    You should have completed at least one introductory programming course (in Java, Python, or a similar language). We expect students to come in with basic familiarity in:

    • Writing simple programs using variables, loops, and conditionals
    • Working with arrays or lists
    • Defining and calling functions or methods
    • Understanding core concepts of object-oriented programming (classes, objects, methods)

    We do not assume prior knowledge of advanced topics like data structures, algorithms, or design patterns. Those are taught in the course, directly in AI-assisted workflows. What matters most is that you're comfortable with the basics of programming and ready to think critically about how to refine AI outputs rather than accept them at face value.

  • Where can I find the syllabus for the course?

    Here's the link to the syllabus.

  • How much time will I need to complete the assignments?

    Students can expect to spend up to 2-4 hours outside of class to complete assignments.

  • Who will the instructors be?

    The course instructors will be professionals from major technology companies, start-ups and academia.

  • What if I need additional help during the class?

    CodePath teaching assistants will be available to help with assignments. You will be in a Slack channel where you can ask questions, and you can also email your instructors if you have further questions.

  • Will I receive a certificate upon completion of the course?

    Yes, you will receive a certificate of completion upon successful completion of the course.

  • Is this class really offered at no-cost? How are you able to do that?

    Yes, CodePath is a nonprofit with backing from major tech companies like Amazon, Google, Meta and Salesforce. Thanks to the generous support of our sponsors, we can offer our classes to any eligible student for no-cost.

  • How does CodePath determine who is admitted to the course?

    We believe our students have the richest experience when we have a cohort of students who are focused on their computer science education and motivated to land an internship or job in the tech industry. To that end, we take a wide array of factors into our admissions decisions including, but not limited to:

    • Performance on our HackerRank Readiness assessment
    • Eligibility criteria (see above section)

  • When will I hear back about the status of my application?

    We process applications on an ongoing basis and generally release decisions within one week of HackerRank completion. All admissions decisions will be made by February 16, 2026.

    If you have not heard back from us, email admissions@codepath.org and our team will get back to you.

  • How can I get more information about this course?

    Please email admissions@codepath.org with any questions.