AI Native Java Developer Hiring Hackathon

We are launching a high-intensity hiring challenge to identify the next generation of AI Native Java Developers — engineers who don’t just write code, but know how to collaborate with AI to build production-grade systems faster and smarter.

If you use tools like Cursor, GitHub Copilot, Claude, ChatGPT, Windsurf, or other AI coding assistants as part of your daily workflow, this hiring drive is designed for you. Build with AI. Code with Speed. Get Hired. “A modern engineering hiring experience designed for AI-native developers.”

We are hiring developers who can:

Build scalable backend systems using Java

Use AI coding assistants effectively

Review, debug, and improve AI-generated code

Think architecturally, not just syntactically

Move fast while maintaining engineering quality

Work like modern AI-first software engineers

This is not a traditional coding test. This is a real-world AI engineering challenge.

Shortlisted candidates will attend an initial screening discussion.

This includes:

Candidate background discussion

AI coding workflow discussion

Live code review exercise

GitHub profile review

Communication and problem-solving evaluation

We are specifically evaluating:

How you think

How you review code

How you use AI tools effectively

Top 20 candidates will be invited for an in-person hackathon where they will build a working software system within 5 hours. (Details will be emailed to the shorlisted candidates)

Candidates must bring:

Their own laptop

Their preferred development setup

Active AI coding assistant tools (Cursor, Copilot, etc.)

We intentionally want candidates working in their own environment because modern engineering productivity depends on developer tooling mastery. What We Will Evaluate

This is NOT just about writing code manually.

We want to observe:

How you collaborate with AI

Prompt engineering for development

Speed of implementation

Architecture decisions

Debugging capability

AI-generated code validation

Engineering judgment

Git discipline

Product thinking

At the end of the hackathon:

Candidates will upload their code to GitHub

Share a public repository link

Submit implementation notes and approach

Our engineering and AI review systems will evaluate:

Code quality

AI usage patterns

System design

Maintainability

Production readiness

Top candidates will then participate in a final engineering interaction focused on:

Architecture choices

AI-assisted development approach

Tradeoffs and scalability

Debugging walkthrough

Ownership mindset

Traditional hiring tests memory. We want to test modern engineering capability.

The best engineers today:

Use AI effectively

Move rapidly

Validate intelligently

Build efficiently

Think critically

This hiring challenge is designed to identify exactly those engineers.